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Land Cover Change and Soil Carbon Regulating Ecosystem Services in the State of South Carolina, USA

Land Cover Change and Soil Carbon Regulating Ecosystem Services in the State of South Carolina, USA Article Land Cover Change and Soil Carbon Regulating Ecosystem Services in the State of South Carolina, USA 1 , 2 2 3 1 Elena A. Mikhailova * , Lili Lin , Zhenbang Hao , Hamdi A. Zurqani , Christopher J. Post , 4 5 Mark A. Schlautman and Gregory C. Post Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC 29634, USA; cpost@clemson.edu University Key Lab for Geomatics Technology and Optimized Resources Utilization, No. 15 Shangxiadian Road, Fuzhou 350002, China; lililin@fafu.edu.cn (L.L.); zhenbanghao@fafu.edu.cn (Z.H.) College of Forestry, Agriculture, and Natural Resources, University of Arkansas at Monticello, Monticello, AR 71655, USA; Zurqani@uamont.edu Department of Environmental Engineering and Earth Sciences, Clemson University, Anderson, SC 29625, USA; mschlau@clemson.edu Geography Department, Portland State University, Portland, OR 97202, USA; grpost@pdx.edu * Correspondence: eleanam@clemson.edu Abstract: Integration of land cover change with soil information is important for valuation of soil carbon (C) regulating ecosystem services (ES) and disservices (ED) and for site-specific land management. The objective of this study was to assess the change in value of regulating ES from soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) stocks, based on the concept of the avoided social cost of carbon dioxide (CO ) emissions for the state of South Carolina (SC) in the United States of America (U.S.A.) by soil order (Soil Taxonomy), land cover, and land cover change (National Land Cover Database, NLCD) using information from the State Soil Geographic (STATSGO) and Soil Survey Geographic Database (SSURGO) databases. Classified Citation: Mikhailova, E.A.; Lin, L.; land cover data for 2001 and 2016 were downloaded from the Multi-Resolution Land Characteristics Hao, Z.; Zurqani, H.A.; Post, C.J.; Consortium (MRLC) website. The total estimated monetary mid-point value for TSC in the state Schlautman, M.A.; Post, G.C. Land of South Carolina was $124.42B (i.e., $124.42 billion U.S. dollars, where B = billion = 10 ) with Cover Change and Soil Carbon the following monetary distribution in 2016 and percent change in value between 2001 and 2016: Regulating Ecosystem Services in the barren land ($259.7M, 9%) (i.e., $259.7 million U.S. dollars, where M = million = 10 ), woody State of South Carolina, USA. Earth wetlands ($33.8B, 1%), shrub/scrub ($3.9B, +9%), mixed forest ($6.9B, +5%), deciduous forest 2021, 2, 674–695. https://doi.org/ ($10.6B, 7%), herbaceous ($4.8B, 5%), evergreen forest ($28.6B, +1%), emergent herbaceous 10.3390/earth2040040 wetlands ($6.9B, 3%), hay/pasture ($7.3B, 10%), cultivated crops ($9.9B, 0%), developed, open space ($7.0B, +5%), developed, medium intensity ($978M, +46%), developed, low intensity ($2.9B, Received: 10 June 2021 +15%), and developed, high intensity ($318M, +39%). The percent change in monetary values was Accepted: 29 August 2021 different from percent change in areas because different soil orders have different TSC contents. Published: 26 September 2021 The percent changes (between 2001 and 2016) both in areas and monetary values varied by soil order and land cover with $1.1B in likely “realized” social cost of C mostly associated with Ultisols Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in ($658.8M). The Midlands region of the state experienced the highest gains in the “high disturbance” published maps and institutional affil- classes and corresponding SC-CO with over $421M for TSC, $354.6M for SOC, and $66.4M for SIC. iations. Among counties, Horry County ranked first with over $142.2M in SC-CO for TSC, followed by Lexington ($103.7M), Richland ($95.3M), Greenville ($81.4M), York ($77.5M), Charleston ($70.7M), Beaufort ($64.1M), Berkeley ($50.9M), Spartanburg ($50.0M), and Aiken ($43.0M) counties. Spatial and temporal analyses of land cover can identify critical locations of soil carbon regulating ecosystem Copyright: © 2021 by the authors. services at risk. Licensee MDPI, Basel, Switzerland. This article is an open access article Keywords: accounting; carbon emissions; CO ; inorganic; organic; regulating; soil; urbanization distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Earth 2021, 2, 674–695. https://doi.org/10.3390/earth2040040 https://www.mdpi.com/journal/earth Earth 2021, 2, FOR PEER REVIEW 2 Earth 2021, 2 675 1. Introduction 1. Introduction Ecosystem services (ES) are the benefits people obtain from nature, which fall into Ecosystem services (ES) are the benefits people obtain from nature, which fall into three categories: provisioning (e.g., food, etc.), regulation/maintenance (e.g., gas regula- three categories: provisioning (e.g., food, etc.), regulation/maintenance (e.g., gas regulation, tion, etc.), and cultural (e.g., recreation, etc.) [1,2]. Ecosystem disservices (ED) are dam- etc.), and cultural (e.g., recreation, etc.) [1,2]. Ecosystem disservices (ED) are damages, ages, which can be of natural and/or anthropogenic origin [2]. Soil carbon is composed of which can be of natural and/or anthropogenic origin [2]. Soil carbon is composed of soil soil organic (SOC) and soil inorganic carbon (SIC) and provides numerous ES (e.g., pro- organic (SOC) and soil inorganic carbon (SIC) and provides numerous ES (e.g., provisioning, visioning, regulating) [3–5]. For example, soil C sequestration is a regulating ES, which regulating) [3–5]. For example, soil C sequestration is a regulating ES, which results in the results in the removal of carbon dioxide (CO2) from the atmosphere and subsequent stor- removal of carbon dioxide (CO ) from the atmosphere and subsequent storage in the soil, age in the soil, thereby avoiding social costs of CO2 (SC-CO2) emissions [3,4]. Common ED thereby avoiding social costs of CO (SC-CO ) emissions [3,4]. Common ED associated 2 2 associated with SOC and SIC is the release of carbon dioxide (CO2) from various uses (e.g., with SOC and SIC is the release of carbon dioxide (CO ) from various uses (e.g., agriculture, agriculture, urbanization, etc.) into the atmosphere, which manifests in realized social SC- urbanization, etc.) into the atmosphere, which manifests in realized social SC-CO [6,7]. CO2 [6,7]. Land cover change analysis is particularly useful in ES/ED assessments because Land cover change analysis is particularly useful in ES/ED assessments because it reveals it reveals “patterns of human activities over time and space as well as the capacities of “patterns of human activities over time and space as well as the capacities of different different ecosystems to provide ES/ED under changing land use” [8–10]. ecosystems to provide ES/ED under changing land use” [8–10]. Traditional ES land cover analysis focuses primarily on land cover changes without Traditional ES land cover analysis focuses primarily on land cover changes without integration of specific soil types and their properties [9]. Integration of land cover analysis integration of specific soil types and their properties [9]. Integration of land cover analysis with soil types allows the identification of hotspots of ES/ED and its temporal changes with soil types allows the identification of hotspots of ES/ED and its temporal changes (Figure 1) [11]. For example, Brown and Quinn (2018) [12] studied the ES change in Up- (Figure 1) [11]. For example, Brown and Quinn (2018) [12] studied the ES change in state South Carolina using the InVEST model [13] to estimate SOC change (among other Upstate South Carolina using the InVEST model [13] to estimate SOC change (among variables). The InVEST model assumes fixed soil carbon values associated with land cover other variables). The InVEST model assumes fixed soil carbon values associated with land classes to assess change and does not leverage the available soil spatial data, which could cover classes to assess change and does not leverage the available soil spatial data, which have helped identify hotspots of ES/ED change and its underlying biophysical character- could have helped identify hotspots of ES/ED change and its underlying biophysical istics (Figure 1). characteristics (Figure 1). Figure 1. The soil “hotspot” concept—an intersection between soil type and land cover classes under Figure 1. The soil “hotspot” concept—an intersection between soil type and land cover classes under natural or anthropogenic dist natural or anthropogenic disturbance urbance (adapt (adapted ed from Bétard and Peulvast from Bétard and Peulvast, , 2019 [14]). 2019 [14]). Previous research on soil C regulating ES in the state of South Carolina estimated Previous research on soil C regulating ES in the state of South Carolina estimated the the avoided social costs of carbon attributed to soil organic carbon (SOC, $107.14B), soil avoided social costs of carbon attributed to soil organic carbon (SOC, $107.14B), soil inor- inorganic carbon (SIC, $17.22B), and total soil carbon (TSC, $124.36B) and was conducted ganic carbon (SIC, $17.22B), and total soil carbon (TSC, $124.36B) and was conducted at at various scales using both biophysical (soil orders) and administrative accounts (state, various scales using both biophysical (soil orders) and administrative accounts (state, re- region, county) [15]. That study used pedodiversity concepts (soil order) integrated with gion, county) [15]. That study used pedodiversity concepts (soil order) integrated with administrative units to report and rank estimates of avoided social costs of soil C, which administrative units to report and rank estimates of avoided social costs of soil C, which provide practical information to design soil C management at various scales [15]. provide practical information to design soil C management at various scales [15]. The present study hypothesizes that pedodiversity concepts overlayed with admin- The present study hypothesizes that pedodiversity concepts overlayed with admin- istrative units (e.g., state) and changes in land cover can be used to identify land cover istrative units (e.g., state) and changes in land cover can be used to identify land cover patterns of soil carbon hotspots for sustainable management (Figure 1). The specific ob- patterns of soil carbon hotspots for sustainable management (Figure 1). The specific ob- jectives of this study are to assess the value of TSC in the state of South Carolina (U.S.A.) jectives of this study are to assess the value of TSC in the state of South Carolina (U.S.A.) by soil order, land cover class, and land cover change based on the social cost of carbon by soil order, land cover class, and land cover change based on the social cost of carbon (SC–CO ) and avoided emissions provided by carbon sequestration, which the U.S. En- (SC–CO2) and avoided emissions provided by carbon sequestration, which the U.S. Envi- vironmental Protection Agency (EPA) has determined to be $46 per metric ton of CO , ronmental Protection Agency (EPA) has determined to be $46 per metric ton of CO2, ap- applicable for the year 2025 based on 2007 U.S. dollars and an average discount rate of plicable for the year 2025 based on 2007 U.S. dollars and an average discount rate of 3% 3% [16]. This study provides monetary values of TSC for soil depth (0–200 cm) across the [16]. This study provides monetary values of TSC for soil depth (0–200 cm) across the state state and different land covers (i.e., barren land, woody wetlands, etc.) using State Soil and different land covers (i.e., barren land, woody wetlands, etc.) using State Soil Geo- Geographic (STATSGO) database, National Land Cover Database (NLCD), and information graphic (STATSGO) database, National Land Cover Database (NLCD), and information previously reported by Guo et al. (2006) [17]. previously reported by Guo et al. (2006) [17]. Earth 2021, 2 676 2. Materials and Methods 2.1. Spatial Analysis Classified land cover data for 2001 and 2016 were downloaded from the Multi- Resolution Land Characteristics Consortium (MRLC) website [18] (Table 1). Changes in land cover were calculated by comparing the 2001 and 2016 land cover maps using the raster calculator function in ArcMap 10.7 [19]. Land cover extent for each of the types of identified land cover (Table 2) was determined by converting the land cover maps to a vector format and then using the union tool with the vector land cover maps and the Soil Survey Geographic database (SSURGO) [20] data layer in ArcMap 10.7 and exporting the results to MS Excel for area analysis (Table 2). The spatial scale of the SSURGO and MRLC is appropriate for associating soil type with land cover change. Table 1. Data sources and descriptions. Data Layer Source Scale/Spatial Resolution (m) Date Google Earth Engine (GEE) data National Land Cover Database (NLCD) provided by the U.S. 30 Geological Survey (USGS) Soil Survey Geographic Database (SSURGO) provided by Soil 10 2016 the National Agricultural Library (NAL) Table 2. The classification system, which is used by National Land Cover Database (NLCD) (adapted from USGS, 2012 [21]). NLCD Land Cover Classes (LULC) Definition Open water All areas of open water, generally with less than 25% cover of vegetation or soil. Thin soil, sand, or rocks, include deserts, dry salt flats, beaches, sand dune, exposed Barren land rock, strip mines, and gravel pits. Forest or shrubland vegetation accounts for >20% of vegetative cover and the soil is Woody wetlands periodically saturated with or covered with water. Natural or semi-natural woody vegetation with aerial stems, generally less than 6 m Shrub/Scrub tall, with individuals or clumps not touching to interlocking. A mixture of broadleaved deciduous and needle-leaved evergreen vegetation, each Mixed forest occupy at least 25% of the area. 75% or more of the tree species shed foliage simultaneously in response to seasonal Deciduous forest change. Plants without persistent stem or shoots above ground and lacking definite firm Herbaceous structure. 75% or more of the tree species maintain their leaves all year. Canopy is never without Evergreen forest green foliage. Emergent herbaceous >75% by herbaceous plants that are hydrophytic or water adapted. wetlands Grasses, legumes, or grass-legume mixtures planted for livestock grazing or the Hay/pasture production of seed or hay crops. Cultivated croplands Areas used to produce crops, such as corn, soybeans, vegetables, tobacco, and cotton. Areas with a mixture of some constructed materials, but mostly vegetation in the form Developed, open space of lawn grasses. Impervious surfaces account for less than 20% of total cover. Areas with a mixture of constructed materials and vegetation. Impervious surfaces Developed, medium account for 50% to 79% of the total cover. These areas most commonly include intensity single-family housing units. Areas with a mixture of constructed materials and vegetation, include single-family Developed, low intensity housing units. Developed, high intensity People reside in high numbers, include apartment complexes and row houses. Earth 2021, 2 677 2.2. The Accounting Framework The present study used both biophysical (science-based) and administrative/land cover (boundary-based) accounts to calculate monetary values for SOC, SIC, and TSC (Tables 3 and 4). Table 3. A conceptual overview of the accounting framework used in this study (adapted from Groshans et al., 2018 [5]). STOCKS FLOWS VALUE Administrative/Land Biophysical Accounts Monetary Account(s) Benefit(s)/Damage(s) Total Value Cover Accounts (Science-Based) (Boundary-Based) Administrative/Land Ecosystem good(s) and Soil extent: Sector: Types of value: cover extent: service(s)/disservices: Composite (total) stock: Total soil carbon (TSC) = Soil organic carbon (SOC) + Soil inorganic carbon (SIC) The social cost of carbon Environment: (SC-CO ) and avoided emissions: - State (South Carolina) - Soil orders (Entisols, - Regions Inceptisols, Histosols, - Regulating (e.g., - $46 per metric ton of CO (2007 - Counties - Sequestered carbon Alfisols, Mollisols, carbon sequestration) U.S. dollars with an average - Land cover classes Spodosols, Ultisols) discount rate of 3% [16]) (LULC) The present study is based on SOC, SIC, and TSC (SOC + SIC) [17] estimates for the SOC, SIC, TSC storage (in Mg or metric tons) and contents (in kg m ) in the contiguous U.S. from Guo et al. (2006) [17]. A monetary valuation for SOC, SIC, TSC was calculated using the social cost of carbon (SC-CO ) of $46 per metric ton of CO , which is applicable 2 2 for 2025 based on 2007 U.S. dollars and an average discount rate of 3% [16]. According to the EPA, the SC-CO is intended to be a comprehensive estimate of climate change damages. Still, it can underestimate the true damages and cost of CO emissions due to the exclusion of various important climate change impacts recognized in the literature [16]. Soil carbon (SC) storage and content estimates were converted to U.S. dollars and dollars per square meter in Microsoft Excel using the following equations, with a social cost of carbon of $46/Mg CO (a metric ton is equivalent to 1 megagram (Mg) or 1000 kilograms (kg)): 44 Mg CO $46 $ = (SC Storage, Mg)   (1) 12 Mg TSC Mg CO $ kg 1 Mg 44 Mg CO $46 = SC Content,    (2) 2 2 3 m m 10 kg 12 Mg TSC Mg CO 2 2 Table 4 presents area-normalized content (kg m ) and monetary values ($ m ) of soil carbon, which were used to estimate TSC storage and TSC value by multiplying corresponding content (values) numbers by an area of a particular soil order within a county, region. For example, for the soil order of Entisols (based on SOC and SIC numbers from Guo et al. (2006) [17]), an area-normalized midpoint TSC content number of 12.8 kgm in the upper 2 m of soil (Table 4) was used to calculate the total TSC storage in soil order by multiplying its area in particular county or region. Then, the reported area-normalized midpoint TSC content number of 12.8 kgm in the upper 2 m (Table 4) was converted to monetary values ($ m ) of TSC using a social cost of carbon (SC-CO ) of $46 per metric ton of CO (2007 U.S. dollars with an average discount rate of 3% [16]), which is $2.17 m and was used to calculate the total monetary value of TSC storage. Earth 2021, 2 678 Earth 2021, 2, FOR PEER REVIEW 5 2 2 Table 4. Area-normalized content (kg m ) and monetary values ($ m ) of soil organic carbon (SOC), soil inorganic carbon (SIC), total soil carbon (TSC) by soil order −2 based on numbers (midpoint −2 soil organic carbon, soil inorganic carbon) in the Table 4. Area-normalized content (kg m ) and monetary values ($ m ) of soil organic carbon (SOC), soil inorganic carbon upper 2-m of the soil from Guo et al., 2006 [17] and a social cost of carbon (SC-CO ) of $46 per metric ton of CO (2007 U.S. (SIC), total soil carbon (TSC) by soil order based on numbers (midpoint soil organic carb 2 on, soil inorganic carbon) in 2 the upper 2-m of dollars with t an he so average il from G discount uo et al., 2006 rate of 3% [17] and a [16]). social cost of carbon (SC-CO2) of $46 per metric ton of CO2 (2007 U.S. dollars with an average discount rate of 3% [16]). Minimum-Midpoint-Maximum Contents Midpoint Values Soil Order Minimum-Midpoint-Maximum Contents Midpoint Values 2 2 2 2 2 2 Soil Order SOC (kg m ) SIC (kg m ) TSC (kg m ) SOC ($ m ) SIC ($ m ) TSC ($ m ) −2 −2 −2 −2 −2 −2 SOC (kg m ) SIC (kg m ) TSC (kg m ) SOC ($ m ) SIC ($ m ) TSC ($ m ) Slightly Weathered Slightly Weathered EntisolsEntisols 1.8 1.8—8.0—15.8 —8.0—15.8 1.9 1.9—4.8—8.4 —4.8—8.4 3 3.7—12.8—24.2 .7—12.8—24.2 1.35 1.35 0.82 0.82 2.17 2.17 Inceptisols 2.8—8.9—17.4 2.5—5.1—8.4 5.3—14.0—25.8 1.50 0.86 2.36 Inceptisols 2.8—8.9—17.4 2.5—5.1—8.4 5.3—14.0—25.8 1.50 0.86 2.36 Histosols 63.9—140.1—243.9 0.6—2.4—5.0 64.5—142.5—248.9 23.62 0.41 24.03 Histosols 63.9—140.1—243.9 0.6—2.4—5.0 64.5—142.5—248.9 23.62 0.41 24.03 Moderately Weathered Moderately Weathered Alfisols 2.3—7.5—14.1 1.3—4.3—8.1 3.6—11.8—22.2 1.27 0.72 1.99 Alfisols 2.3—7.5—14.1 1.3—4.3—8.1 3.6—11.8—22.2 1.27 0.72 1.99 Mollisols 5.9—13.5—22.8 4.9—11.5—19.7 10.8—25.0—42.5 2.28 1.93 4.21 Mollisols 5.9—13.5—22.8 4.9—11.5—19.7 10.8—25.0—42.5 2.28 1.93 4.21 Strongly Weathered Strongly Weathered Spodosols 2.9—12.3—25.5 0.2—0.6—1.1 3.1—12.9—26.6 2.07 0.10 2.17 Spodosols 2.9—12.3—25.5 0.2—0.6—1.1 3.1—12.9—26.6 2.07 0.10 2.17 Ultisols 1.9—7.1—13.9 0.0—0.0—0.0 1.9—7.1—13.9 1.20 0.00 1.20 Ultisols 1.9—7.1—13.9 0.0—0.0—0.0 1.9—7.1—13.9 1.20 0.00 1.20 Note: TSC = SOC + SIC. Note: TSC = SOC + SIC. 3. Results 3. Results 3.1. Land Cover Change in South Carolina 3.1. Land Cover Change in South Carolina Land cover in the state of South Carolina is diverse (Figure 2) with the following Land cover in the state of South Carolina is diverse (Figure 2) with the following distri- distribution including open water (2016, in decreasing order): evergreen forest (23.85%); bution including open water (2016, in decreasing order): evergreen forest (23.85%); woody woody wetlands (19.37%); cultivated croplands (9.05%); deciduous forest (8.84%); wetlands (19.37%); cultivated croplands (9.05%); deciduous forest (8.84%); hay/pasture hay/pasture (6.58%); open water (6.34%); developed, open space (6.12%); mixed forest (6.58%); open water (6.34%); developed, open space (6.12%); mixed forest (5.77%); herba- (5.77%); herbaceous (3.99%); shrub/scrub (3.28%); developed, low intensity (2.70%); emer- ceous (3.99%); shrub/scrub (3.28%); developed, low intensity (2.70%); emergent herbaceous gent herbaceous wetlands (2.61%); developed, medium intensity (0.92%); developed, high wetlands (2.61%); developed, medium intensity (0.92%); developed, high intensity (0.34%); intensity (0.34%); and barren land (0.25%) (Figure 2). Land cover in South Carolina has and barren land (0.25%) (Figure 2). Land cover in South Carolina has undergone changes undergone changes between 2001 and 2016, with a general trend of urbanization leading between 2001 and 2016, with a general trend of urbanization leading to losses in “low to losses in “low disturbance” land covers (e.g., deciduous forest) and gains in developed disturbance” land covers (e.g., deciduous forest) and gains in developed land cover classes land cover classes (e.g., open, low, medium, and high intensity) (Table 5). (e.g., open, low, medium, and high intensity) (Table 5). Figure 2. Land cover map (2016) of South Carolina (SC) (U.S.A.) (33.8361 N, 81.1637 W) (adapted Figure 2. Land cover map (2016) of South Carolina (SC) (U.S.A.) (33.8361° N, 81.1637° W) (adapted from Multi-Resolution Land Characteristics Consortium [18]). from Multi-Resolution Land Characteristics Consortium [18]). Earth 2021, 2 679 Table 5. Land cover change in South Carolina (U.S.A.) between 2001 and 2016 (based on Multi- Resolution Land Characteristics Consortium [18]). 2 2 NLCD Land Cover Classes (LULC) 2001 Area (km ) 2016 Area (km ) Change (%) Barren land 176.71 162.44 8 Woody wetlands 15,944.41 15,769.88 1 Shrub/Scrub 2510.33 2702.75 +8 Mixed forest 4462.97 4717.34 +6 Deciduous forest 7797.41 7231.86 7 Herbaceous 3297.11 3250.81 1 Evergreen forest 19,590.27 19,637.64 0 Emergent herbaceous wetlands 2006.92 2037.10 +2 Hay/Pasture 6014.94 5415.81 10 Cultivated crops 7475.67 7452.51 0 Developed, open space 4671.20 4913.96 +5 Developed, medium intensity 464.39 673.16 +45 Developed, low intensity 1826.46 2080.79 +14 Developed, high intensity 161.60 225.32 +39 3.2. Land Cover Change and Soil Carbon Regulating Ecosystem Services in South Carolina The total estimated mid-point monetary value for TSC in South Carolina in 2016 was $124.4B (i.e., $124.4 billion U.S. dollars, where B = billion = 10 ), with the following mone- tary distribution and percent change in value between 2001 and 2016: barren land ($259.7M, 9%) (i.e., $259.7 million U.S. dollars, where M = million = 10 ); woody wetlands ($33.8B, 1%); shrub/scrub ($3.9B, +9%); mixed forest ($6.9B, +5%); deciduous forest ($10.6B, 7%); herbaceous ($4.8B, 5%); evergreen forest ($28.6B, +1%); emergent herbaceous wetlands ($6.9B, 3%); hay/pasture ($7.3B, 10%); cultivated crops ($9.9B, 0%); developed, open space ($7.0B, +5%); developed, medium intensity ($978M, +46%); developed, low intensity ($2.9B, +15%); and developed, high intensity ($318M, +39%) (Table 6). For a particular land cover, the percent change in monetary value can be different from the percent change in area because different soil orders have different TSC contents. Most soil orders experienced losses in “low disturbance” land covers and gains in developed land cover classes with $1.1B in likely “realized” social cost of carbon. Spodosols experienced dramatic increases (% change) in the developed land covers (Table 7). Table 6. Soil carbon regulating ecosystem services and land cover change in the state of South Carolina (U.S.A.) between 2001 and 2016 based on total soil carbon (TSC) derived from numbers in the upper 2 m of the soil (by soil type) from Guo et al., 2006 [17] and a social cost of carbon (SC-CO ) of $46 per metric ton of CO (2007 U.S. dollars with an average discount 2 2 rate of 3% [16]). NLCD Land Cover Classes (LULC) 2001 Mid. Value ($) 2016 Mid. Value ($) Net Change ($) Change (%) Barren land $284,320,000 $259,710,000 $24,610,000 9 Woody wetlands $34,199,940,000 $33,808,480,000 $391,460,000 1 Shrub/Scrub $3,612,540,000 $3,922,480,000 $309,940,000 +9 Mixed forest $6,587,920,000 $6,946,750,000 $358,830,000 +5 Deciduous forest $11,409,990,000 $10,588,350,000 $821,640,000 7 Herbaceous $5,054,090,000 $4,800,100,000 $253,990,000 5 Evergreen forest $28,470,170,000 $28,641,620,000 $171,450,000 +1 Emergent herbaceous wetlands $7,110,200,000 $6,930,830,000 $179,370,000 3 Hay/Pasture $8,165,800,000 $7,330,030,000 $835,770,000 10 Cultivated crops $9,890,030,000 $9,902,780,000 $12,750,000 0 Developed, open space $6,645,820,000 $7,001,100,000 $355,280,000 +5 Developed, medium intensity $670,520,000 $978,030,000 $307,510,000 +46 Developed, low intensity $2,610,130,000 $2,991,170,000 $381,040,000 +15 Developed, high intensity $228,710,000 $318,150,000 $89,440,000 +39 Note: Mid. = midpoint. Earth 2021, 2 680 Table 7. Land cover change in the state of South Carolina (U.S.A.) between 2001 and 2016 based on soil order. Degree of Weathering and Soil Development Earth 2021, 2, FOR PEER REVIEW 7 Slight Moderate Strong NLCD Land Cover Classes (LULC) Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols Area Change between 2001 and 2016 (%) Table 7. Land cover change in the state of South Carolina (U.S.A.) between 2001 and 2016 based on soil order. Barren land 10.16% 3.10% 15.52% 1.36% 14.12% 10.67% 8.01% Degree of Weathering and Soil Development Woody wetlands 0.48% 1.14% 1.45% 1.59% 0.38% 2.11% 1.24% Slight Moderate Strong Shrub/Scrub 5.56% 32.23% 59.69% 20.51% 53.78% 30.04% 6.57% NLCD Land Cover Classes Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols (LULC) Mixed forest 4.12% 5.29% 12.05% 8.88% 2.88% 1.86% 5.96% Area Change between 2001 and 2016 (%) Deciduous forest 4.45% 6.06% 17.94% 10.67% 0.95% 18.30% 7.11% Barren land −10.16% −3.10% −15.52% −1.36% −14.12% −10.67% −8.01% Herbaceous 5.33% 8.21% 30.16% 27.77% 25.13% 27.32% 4.46% Woody wetlands 0.48% −1.14% −1.45% −1.59% 0.38% −2.11% −1.24% Evergreen forest Shrub/Scrub 5. 1.86% 56% 0.43% 32.23% 7.47%59.69% 20.51% 4.34% −53.78% 6.72% −30.04% 6. 57% 0.71% 0.25% Mixed forest −4.12% 5.29% 12.05% 8.88% 2.88% −1.86% 5.96% Emergent herbaceous wetlands 1.20% 12.41% 7.00% 4.76% 20.64% 3.28% 28.35% Deciduous forest −4.45% −6.06% −17.94% −10.67% 0.95% 18.30% −7.11% Hay/Pasture 11.31% 11.23% 45.01% 12.96% 36.19% 17.19% 9.47% Herbaceous −5.33% −8.21% −30.16% −27.77% −25.13% −27.32% 4.46% Cultivated crops 3.74% 1.33% 5.13% 10.99% 42.90% 0.51% 0.66% Evergreen forest −1.86% 0.43% 7.47% 4.34% 6.72% −0.71% −0.25% Emergent herbaceous wetlands −1.20% 12.41% −7.00% −4.76% −20.64% −3.28% 28.35% Developed, open space 5.50% 5.02% 1.41% 7.43% 9.41% 7.79% 4.88% Hay/Pasture −11.31% −11.23% −45.01% −12.96% −36.19% −17.19% −9.47% Developed, medium intensity 41.72% 38.84% 16.04% 58.07% 65.03% 68.42% 43.36% Cultivated crops 3.74% 1.33% 5.13% 10.99% 42.90% −0.51% −0.66% Developed, low intensity 17.24% 11.30% 3.24% 20.49% 19.75% 31.88% 12.38% Developed, open space 5.50% 5.02% 1.41% 7.43% 9.41% 7.79% 4.88% Developed, high intensity 37.53% 35.67% 16.26% 40.92% 22.54% 78.26% 38.60% Developed, medium intensity 41.72% 38.84% 16.04% 58.07% 65.03% 68.42% 43.36% Developed, low intensity 17.24% 11.30% 3.24% 20.49% 19.75% 31.88% 12.38% Developed, high intensity 37.53% 35.67% 16.26% 40.92% 22.54% 78.26% 38.60% Integration of land cover change analysis with soil carbon regulating ecosystem servicesIallows ntegratioidentification n of land cover cof han “hotspots” ge analysis w of ithpotential soil carbon“r reealized” gulating ec ro egulating system ser-ED due to vices allows identification of “hotspots” of potential “realized” regulating ED due to land land disturbance, which can be described by land cover classes (e.g., developed with disturbance, which can be described by land cover classes (e.g., developed with various various degrees of intensity: open, low, medium, and high). The area of soil type (e.g., degrees of intensity: open, low, medium, and high). The area of soil type (e.g., soil order soil order in this study, Figure 3) can be used to calculate the soil C regulating ES (e.g., the in this study, Figure 3) can be used to calculate the soil C regulating ES (e.g., the social social cost of carbon, SC-CO ) using soil C contents for specific soil orders. There is no cost of carbon, SC-CO2) using soil C contents for specific soil orders. There is no direct direct relationship between the area of the soil order and the social costs of soil C because relationship between the area of the soil order and the social costs of soil C because differ- different soil orders have different soil C contents (Figure 3a,b). ent soil orders have different soil C contents (Figure 3a,b). Figure 3. Soil distribution in the state of South Carolina (U.S.A.) (2016): (a) area of soil orders; (b) value ($) of soil carbon Figure 3. Soil distribution in the state of South Carolina (U.S.A.) (2016): (a) area of soil orders; (b) value ($) of soil carbon regulating ecosystem services based on total soil carbon (TSC) derived from numbers in the upper 2 m of the soil from regulating ecosystem services based on total soil carbon (TSC) derived from numbers in the upper 2 m of the soil from Guo et al., 2006 [17] and a social cost of carbon (SC-CO2) of $46 per metric ton of CO2 (2007 U.S. dollars with an average Guo et al., 2006 [17] and a social cost of carbon (SC-CO ) of $46 per metric ton of CO (2007 U.S. dollars with an average 2 2 discount rate of 3% [16]). discount rate of 3% [16]). Earth 2021, 2 681 Land cover change (between 2001 and 2016) resulted in $1.1B in most likely “realized” SC-CO with the following distribution by soil order: Entisols: $173.6M, 15% from the total; Inceptisols: $54.3M, 5%; Histosols: $0.0, 0%; Alfisols: $151.2M, 13%; Mollisols: $4.2M, 1%; Spodosols: $91.1M, 8%; Ultisols: $658.8M, 58%) (Table 8). Most of the potential “realized” social costs of C were associated with Ultisols, a highly weathered soil covering the largest proportion of the state area (70%). Table 8. Soil carbon regulating ecosystem services and “developed” land cover change in the state of South Carolina (U.S.A.) between 2001 and 2016 based on total soil carbon (TSC) derived from numbers in the upper 2 m of the soil from Guo et al., 2006 [17] and a social cost of carbon (SC-CO ) of $46 per metric ton of CO (2007 U.S. dollars with an average discount rate 2 2 of 3% [16]). Degree of Weathering and Soil Development Slight Moderate Strong NLCD Land Cover Classes (LULC) Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols SC-CO ($) 7 7 7 6 7 8 Developed, open space 4.1  10 2.1  10 0 4.8  10 4.2  10 2.6  10 2.1  10 7 7 7 7 8 Developed, medium intensity 0 0 5.2  10 1.4  10 4.0  10 2.4  10 1.8  10 7 7 7 7 8 Developed, low intensity 6.7  10 1.7  10 0 5.4  10 0 3.5  10 2.1  10 7 6 7 6 7 Developed, high intensity 1.3  10 2.4  10 0 1.0  10 0 6.5  10 5.8  10 9 8 7 8 6 7 8 Totals ($1.1  10 ) 1.7  10 5.4  10 0 1.5  10 4.2  10 9.1  10 6.6  10 3.3. Land Cover Change and Soil Carbon Regulating Ecosystem Services by Soil Order, County, and Region South Carolina experienced changes in land use/land cover (LULC) over the 15-year period from 2001 to 2016, and these changes varied by soil order, county, and region. Most regions and counties experienced area losses in “low disturbance” LULC classes and gains in the “high disturbance” LULC classes (Table 9), which resulted in potential “realized” social costs of carbon from SOC (Tables 10 and 11), SIC (Tables 12 and 13), and TSC (Tables 14 and 15). The Midlands region of the state experienced the highest gains in the “high disturbance” LULC classes and corresponding potential “realized” SC-CO with over $421M for TSC, $354.6M for SOC, and $66.4M for SIC. Among counties, Horry County ranked first with over $142.2M in potential “realized” SC-CO for TSC, followed by Lexington ($103.7M), Richland ($95.3M), Greenville ($81.4M), York ($77.5M), Charleston ($70.7M), Beaufort ($64.1M), Berkeley ($50.9M), Spartanburg ($50.0M), and Aiken ($43.0M) counties. Increases in “high disturbance” LULC classes are commonly associated with already existing urbanized areas in all regions, and several coastal counties. In addition to totals of potential “realized” SC-CO by county, this study also provides a breakdown of SC-CO by soil order for each county. This novel way to combine satellite based LULC change analysis with soil information can provide useful insight on the intersection between LULC change and soil resources. The benefits of this approach can be demonstrated using the results of this study, which shows that most of the potential “realized” SC-CO are associated with the soil order of Ultisols, which are highly leached and erodible soils. Previous research by Werts et al. (2013) [22] has shown that the development process can have additional social costs associated with this land conversion (e.g., erosion, water quality, and human social costs). Spodosols are present in many rapidly developing coastal areas of South Carolina but may not be suitable for development that includes septic systems because of their high saturated conductivity [23]. Several counties had development in agriculturally productive soils (Alfisols and Mollisols) which are commonly considered to be soil carbon hotspots with higher SC-CO . In South Carolina, these agriculturally productive soils are relatively rare and represent less than 10% of the total state area, compared to Ultisols, which cover 70% of South Carolina [15]. Over time, loss of productive agricultural soils to development can reduce local food security. Future planning should consider different soil types to limit the social cost of Earth 2021, 2 682 development. For example, government protections of wetland soils (e.g., Histosols) have limited their development in South Carolina (Table 15). Table 9. Soil diversity (pedodiversity) and “developed” land cover (open space, low, medium, and high intensity) change from 2001 to 2016 by soil order (taxonomic pedodiversity), region, and county in the state of South Carolina (U.S.A.) based on Soil Survey Geographic (SSURGO) Database (2020) [20]. Degree of Weathering and Soil Development Increase in Developed County Area (Region) Slightly Weathered Moderately Weathered Strongly Weathered (km ) Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols (Rank) Increase in Developed Area (km ) between 2001 and 2016 by Soil Order Anderson 30.0 (11) 0.5 0 0 0 0 0 29.5 Cherokee 7.3 (24) 0.6 0.2 0 0.1 0 0 6.4 Greenville 65.5 (2) 1.2 1.5 0 0 0 0 62.7 Oconee 14.4 (16) 0.3 0.2 0 0 0 0 13.9 Pickens 12.4 (17) 0.2 0.1 0 0 0 0 12.1 Spartanburg 39.5 (6) 0.4 1.6 0 0.5 0 0 37 Union 4.2 (28) 0.1 0 0 0.7 0 0 3.4 (Upstate) 173.3 (2) 3.3 3.6 0 1.3 0 0 165 Abbeville 3.2 (32) 0.0 0.0 0 0.6 0 0 2.6 Aiken 30.7 (10) 5.2 0.7 0 0.4 0 0 24.4 Chester 3.7 (30) 0 0.1 0 1.3 0 0 2.3 Edgefield 6.1 (25) 0.7 0.3 0 0.1 0 0 5.0 Fairfield 4.2 (29) 0 0.1 0 0.4 0 0 3.7 Greenwood 8.4 (22) 0 0.2 0 2.3 0 0 5.7 Kershaw 11.9 (19) 3.5 0.2 0 0.1 0 0 8.1 Lancaster 20.8 (12) 0.3 1.0 0 0.4 0 0 19.1 Laurens 8.9 (21) 0.1 0 0 1 0 0 7.8 Lexington 65.4 (3) 22.0 2.6 0 1 0 0.1 39.7 Newberry 4.6 (27) 0 0 0 0.2 0 0 4.4 Richland 63.4 (4) 15.8 2.3 0 1.5 0 0 43.8 Saluda 3.1 (33) 0 0 0 0.2 0 0 2.9 York 51.6 (5) 0.1 0.7 0 18.6 0 0 32.2 (Midlands) 285.8 (1) 47.7 8.2 0 28.1 0 0.1 201.7 Chesterfield 2.9 (34) 0.1 0.6 0 0 0 0 2.2 Clarendon 3.6 (31) 0.1 0.1 0 0 0 0 3.4 Darlington 7.4 (23) 0.4 0.7 0 0 0 0 6.3 Dillon 2.2 (35) 0.3 0 0 0 0 0 1.9 Florence 19.7 (14) 0.2 0.6 0 0 0 0 18.9 Georgetown 9.7 (20) 1.8 1.5 0 2.6 0 1.0 2.8 Horry 84.1 (1) 6.3 3.2 0 10.9 0 23.6 40.1 Lee 1.5 (41) 0.2 0 0 0 0 0 1.3 Marion 1.5 (42) 0 0 0 0 0 0 1.5 Marlboro 1.6 (40) 0 0.1 0 0 0 0 1.5 Sumter 15.4 (15) 0 0.3 0 0 0 0 15.1 Williamsburg 1.9 (38) 0.1 0.1 0 0 0 0 1.7 151.5 (4) 9.5 7.2 0 13.5 0 24.6 96.7 (Pee Dee) Allendale 0.9 (44) 0 0 0 0 0 0 0.9 Bamberg 0.5 (46) 0 0 0 0 0 0 0.5 Barnwell 2.2 (36) 0.3 0 0 0 0 0 1.9 Beaufort 35.8 (9) 9.7 0.8 0 0.9 0.3 9.5 14.6 Berkeley 37.4 (8) 1.3 0.3 0 3.6 0 1.6 30.6 Calhoun 1.8 (39) 0.1 0.1 0 0 0 0 1.6 Charleston 37.5 (7) 5.5 2.1 0 18.8 0 3.2 7.9 Colleton 2.0 (37) 0.2 0.1 0 0 0 0.7 1.0 Dorchester 20.8 (13) 1.3 0 0 7.7 0 0.1 11.7 Hampton 0.6 (45) 0 0 0 0 0 0 0.5 Jasper 12.1 (18) 0.9 0 0 1.8 0.1 0.5 8.8 McCormick 1.3 (43) 0 0 0 0.2 0 0 1.1 Orangeburg 5.8 (26) 0.1 0 0 0.1 0 0 5.6 (Low Country) 158.6 (3) 19.4 3.4 0 33.1 0.4 15.6 86.7 Totals 769.1 79.9 22.4 0 76.0 0.4 40.3 550.1 Earth 2021, 2 683 Table 10. Mid-point soil organic carbon (SOC) storage for newly “developed” land covers (open space, low, medium, and high intensity) from 2001 to 2016 in the state of South Carolina (U.S.A.) based on mid-point SOC content numbers in the upper 2 m of the soil based on data from Guo et al., 2006 [17]. Degree of Weathering and Soil Development County Total SOC Slightly Weathered Moderately Weathered Strongly Weathered (Region) (kg) Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols SOC (kg) 8 6 8 Anderson 2.13  10 4.00  10 0 0 0 0 0 2.09  10 7 6 6 5 7 Cherokee 5.28  10 4.80  10 1.78  10 0 7.50  10 0 0 4.54  10 8 6 7 8 Greenville 0 0 0 0 4.68  10 9.60  10 1.34  10 4.45  10 8 6 6 7 Oconee 0 0 0 0 1.03  10 2.40  10 1.78  10 9.87  10 7 6 5 7 Pickens 8.84  10 1.60  10 8.90  10 0 0 0 0 8.59  10 8 6 7 6 8 Spartanburg 2.84  10 3.20  10 1.42  10 0 3.75  10 0 0 2.63  10 7 5 6 7 Union 3.02  10 8.00  10 0 0 5.25  10 0 0 2.41  10 9 7 7 6 9 (Upstate) 1.24  10 2.64  10 3.20  10 0 9.75  10 0 0 1.17  10 7 6 7 Abbeville 0 0 0 0 0 2.30  10 4.50  10 1.85  10 8 7 6 6 8 Aiken 2.24  10 4.16  10 6.23  10 0 3.00  10 0 0 1.73  10 7 5 6 7 Chester 2.70  10 0 8.90  10 0 9.75  10 0 0 1.63  10 7 6 6 5 7 Edgefield 4.45  10 5.60  10 2.67  10 0 7.50  10 0 0 3.55  10 7 5 6 7 Fairfield 3.02  10 0 8.90  10 0 3.00  10 0 0 2.63  10 7 6 7 7 Greenwood 5.95  10 0 1.78  10 0 1.73  10 0 0 4.05  10 7 7 6 5 7 Kershaw 8.80  10 2.80  10 1.78  10 0 7.50  10 0 0 5.75  10 8 6 6 6 8 Lancaster 0 0 0 1.50  10 2.40  10 8.90  10 3.00  10 1.36  10 7 5 6 7 Laurens 0 0 0 0 6.37  10 8.00  10 7.50  10 5.54  10 8 8 7 6 6 8 Lexington 4.90  10 1.76  10 2.31  10 0 7.50  10 0 1.23  10 2.82  10 7 6 7 Newberry 3.27  10 0 0 0 1.50  10 0 0 3.12  10 8 8 7 7 8 Richland 4.69  10 1.26  10 2.05  10 0 1.13  10 0 0 3.11  10 7 6 7 Saluda 2.21  10 0 0 0 1.50  10 0 0 2.06  10 8 5 6 8 8 York 3.75  10 8.00  10 6.23  10 0 1.40  10 0 0 2.29  10 9 8 7 8 6 9 (Midlands) 2.10  10 3.82  10 7.30  10 0 2.11  10 0 1.23  10 1.43  10 7 5 6 7 Chesterfield 2.18  10 8.00  10 5.34  10 0 0 0 0 1.56  10 7 5 5 7 Clarendon 2.58  10 8.00  10 8.90  10 0 0 0 0 2.41  10 7 6 6 7 Darlington 5.42  10 3.20  10 6.23  10 0 0 0 0 4.47  10 7 6 7 Dillon 1.59  10 2.40  10 0 0 0 0 0 1.35  10 8 6 6 8 Florence 1.41  10 1.60  10 5.34  10 0 0 0 0 1.34  10 7 7 7 7 7 7 Georgetown 7.94  10 1.44  10 1.34  10 0 1.95  10 0 1.23  10 1.99  10 8 7 7 7 8 8 Horry 0 0 7.36  10 5.04  10 2.85  10 8.18  10 2.90  10 2.85  10 7 6 6 Lee 1.08  10 1.60  10 0 0 0 0 0 9.23  10 7 7 Marion 1.07  10 0 0 0 0 0 0 1.07  10 7 5 7 Marlboro 1.15  10 0 8.90  10 0 0 0 0 1.07  10 8 6 8 Sumter 1.10  10 0 2.67  10 0 0 0 0 1.07  10 7 5 5 7 Williamsburg 1.38  10 8.00  10 8.90  10 0 0 0 0 1.21  10 9 7 7 8 8 8 (Pee Dee) 1.23  10 7.60  10 6.41  10 0 1.01  10 0 3.03  10 6.87  10 6 6 Allendale 6.39  10 0 0 0 0 0 0 6.39  10 6 6 Bamberg 3.55  10 0 0 0 0 0 0 3.55  10 7 6 7 Barnwell 1.59  10 2.40  10 0 0 0 0 0 1.35  10 8 7 6 6 6 8 8 Beaufort 3.16  10 7.76  10 7.12  10 0 6.75  10 4.05  10 1.17  10 1.04  10 8 7 6 7 7 8 Berkeley 2.77  10 1.04  10 2.67  10 0 2.70  10 0 1.97  10 2.17  10 7 5 5 7 Calhoun 1.31  10 8.00  10 8.90  10 0 0 0 0 1.14  10 8 7 7 8 7 7 Charleston 0 0 2.99  10 4.40  10 1.87  10 1.41  10 3.94  10 5.61  10 7 6 5 6 6 Colleton 1.82  10 1.60  10 8.90  10 0 0 0 8.61  10 7.10  10 8 7 7 6 7 Dorchester 1.52  10 1.04  10 0 0 5.78  10 0 1.23  10 8.31  10 6 6 Hampton 3.55  10 0 0 0 0 0 0 3.55  10 7 6 7 6 6 7 Jasper 9.07  10 7.20  10 0 0 1.35  10 1.35  10 6.15  10 6.25  10 6 6 6 McCormick 9.31  10 0 0 0 1.50  10 0 0 7.81  10 7 5 5 7 Orangeburg 4.13  10 8.00  10 0 0 7.50  10 0 0 3.98  10 9 8 7 8 6 8 8 (Low Country) 1.25  10 1.55  10 3.03  10 0 2.48  10 5.40  10 1.92  10 6.16  10 9 8 8 8 6 8 9 Totals 5.82  10 6.39  10 1.99  10 0 5.70  10 5.40  10 4.96  10 3.91  10 Earth 2021, 2 684 Table 11. The total dollar value of mid-point soil organic carbon (SOC) storage value for newly “developed” land covers (open space, low, medium, and high intensity) from 2001 to 2016 in the state of South Carolina (U.S.A.) based on a social cost of carbon (SC-CO ) of $46 per metric ton of CO (2007 U.S. dollars with an average discount rate of 3% [16]). 2 2 Degree of Weathering and Soil Development Total County SC-CO 2 Slightly Weathered Moderately Weathered Strongly Weathered (Region) ($) Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols SC-CO ($) 7 5 7 Anderson 3.61  10 6.75  10 0 0 0 0 0 3.54  10 6 5 5 5 6 Cherokee 8.92  10 8.10  10 3.00  10 0 1.27  10 0 0 7.68  10 7 6 6 7 Greenville 0 0 0 0 7.91  10 1.62  10 2.25  10 7.52  10 7 5 5 7 Oconee 0 0 0 0 1.74  10 4.05  10 3.00  10 1.67  10 7 5 5 7 Pickens 1.49  10 2.70  10 1.50  10 0 0 0 0 1.45  10 7 5 6 5 7 Spartanburg 4.80  10 5.40  10 2.40  10 0 6.35  10 0 0 4.44  10 6 5 5 6 Union 5.10  10 1.35  10 0 0 8.89  10 0 0 4.08  10 8 6 6 6 8 (Upstate) 2.10  10 4.46  10 5.40  10 0 1.65  10 0 0 1.98  10 6 5 6 Abbeville 0 0 0 0 0 3.88  10 7.62  10 3.12  10 7 6 6 5 7 Aiken 3.79  10 7.02  10 1.05  10 0 5.08  10 0 0 2.93  10 6 5 6 6 Chester 4.56  10 0 1.50  10 0 1.65  10 0 0 2.76  10 6 5 5 5 6 Edgefield 7.52  10 9.45  10 4.50  10 0 1.27  10 0 0 6.00  10 6 5 5 6 Fairfield 5.10  10 0 1.50  10 0 5.08  10 0 0 4.44  10 7 5 6 6 Greenwood 1.01  10 0 3.00  10 0 2.92  10 0 0 6.84  10 7 6 5 5 6 Kershaw 1.49  10 4.73  10 3.00  10 0 1.27  10 0 0 9.72  10 7 5 6 5 7 Lancaster 0 0 0 2.53  10 4.05  10 1.50  10 5.08  10 2.29  10 7 5 6 6 Laurens 0 0 0 0 1.08  10 1.35  10 1.27  10 9.36  10 7 7 6 6 5 7 Lexington 8.27  10 2.97  10 3.90  10 0 1.27  10 0 2.07  10 4.76  10 6 5 6 Newberry 5.53  10 0 0 0 2.54  10 0 0 5.28  10 7 7 6 6 7 Richland 7.92  10 2.13  10 3.45  10 0 1.91  10 0 0 5.26  10 6 5 6 Saluda 3.73  10 0 0 0 2.54  10 0 0 3.48  10 7 5 6 7 7 York 6.34  10 1.35  10 1.05  10 0 2.36  10 0 0 3.86  10 8 7 7 7 5 8 (Midlands) 3.55  10 6.44  10 1.23  10 0 3.57  10 0 2.07  10 2.42  10 6 5 5 6 Chesterfield 3.68  10 1.35  10 9.00  10 0 0 0 0 2.64  10 6 5 5 6 Clarendon 4.37  10 1.35  10 1.50  10 0 0 0 0 4.08  10 6 5 6 6 Darlington 9.15  10 5.40  10 1.05  10 0 0 0 0 7.56  10 6 5 6 Dillon 2.69  10 4.05  10 0 0 0 0 0 2.28  10 7 5 5 7 Florence 2.39  10 2.70  10 9.00  10 0 0 0 0 2.27  10 7 6 6 6 6 6 Georgetown 1.34  10 2.43  10 2.25  10 0 3.30  10 0 2.07  10 3.36  10 8 6 6 7 7 7 Horry 0 0 1.24  10 8.51  10 4.80  10 1.38  10 4.89  10 4.81  10 6 5 6 Lee 1.83  10 2.70  10 0 0 0 0 0 1.56  10 6 6 Marion 1.80  10 0 0 0 0 0 0 1.80  10 6 5 6 Marlboro 1.95  10 0 1.50  10 0 0 0 0 1.80  10 7 5 7 Sumter 1.86  10 0 4.50  10 0 0 0 0 1.81  10 6 5 5 6 Williamsburg 2.33  10 1.35  10 1.50  10 0 0 0 0 2.04  10 8 7 7 7 7 8 (Pee Dee) 2.08  10 1.28  10 1.08  10 0 1.71  10 0 5.09  10 1.16  10 6 6 Allendale 1.08  10 0 0 0 0 0 0 1.08  10 5 5 Bamberg 6.00  10 0 0 0 0 0 0 6.00  10 6 5 6 Barnwell 2.69  10 4.05  10 0 0 0 0 0 2.28  10 7 7 6 6 5 7 7 Beaufort 5.33  10 1.31  10 1.20  10 0 1.14  10 6.84  10 1.97  10 1.75  10 7 6 5 6 6 7 Berkeley 4.68  10 1.76  10 4.50  10 0 4.57  10 0 3.31  10 3.67  10 6 5 5 6 Calhoun 2.21  10 1.35  10 1.50  10 0 0 0 0 1.92  10 7 6 6 7 6 6 Charleston 0 0 5.06  10 7.43  10 3.15  10 2.39  10 6.62  10 9.48  10 6 5 5 6 6 Colleton 3.07  10 2.70  10 1.50  10 0 0 0 1.45  10 1.20  10 7 6 6 5 7 Dorchester 2.58  10 1.76  10 0 0 9.78  10 0 2.07  10 1.40  10 5 5 Hampton 6.00  10 0 0 0 0 0 0 6.00  10 7 6 6 5 6 7 Jasper 1.53  10 1.22  10 0 0 2.29  10 2.28  10 1.04  10 1.06  10 6 5 6 McCormick 1.57  10 0 0 0 2.54  10 0 0 1.32  10 6 5 5 6 Orangeburg 6.98  10 1.35  10 0 0 1.27  10 0 0 6.72  10 8 7 6 7 5 7 8 (Low Country) 2.11  10 2.62  10 5.10  10 0 4.20  10 9.12  10 3.23  10 1.04  10 8 8 7 7 5 7 8 Totals 9.82  10 1.08  10 3.36  10 0 9.65  10 9.12  10 8.34  10 6.60  10 Earth 2021, 2 685 Table 12. Mid-point soil inorganic carbon (SIC) storage for newly “developed” land covers (open space, low, medium, and high intensity) from 2001 to 2016 in the state of South Carolina (U.S.A.) based on mid-point SIC content numbers in the upper 2 m of the soil based on data from Guo et al., 2006 [17]. Degree of Weathering and Soil Development County Total SIC Slightly Weathered Moderately Weathered Strongly Weathered (Region) (kg) Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols SIC (kg) 6 6 Anderson 2.40  10 2.40  10 0 0 0 0 0 0 6 6 6 5 Cherokee 4.33  10 2.88  10 1.02  10 0 4.30  10 0 0 0 7 6 6 Greenville 0 0 0 0 0 1.34  10 5.76  10 7.65  10 6 6 6 Oconee 0 0 0 0 0 2.46  10 1.44  10 1.02  10 6 5 5 Pickens 1.47  10 9.60  10 5.10  10 0 0 0 0 0 7 6 6 6 Spartanburg 1.22  10 1.92  10 8.16  10 0 2.15  10 0 0 0 6 5 6 Union 3.49  10 4.80  10 0 0 3.01  10 0 0 0 7 7 7 6 (Upstate) 3.98  10 1.58  10 1.84  10 0 5.59  10 0 0 0 6 6 Abbeville 0 0 0 0 0 0 2.58  10 2.58  10 7 7 6 6 Aiken 3.03  10 2.50  10 3.57  10 0 1.72  10 0 0 0 6 5 6 Chester 6.10  10 0 5.10  10 0 5.59  10 0 0 0 6 6 6 5 Edgefield 5.32  10 3.36  10 1.53  10 0 4.30  10 0 0 0 6 5 6 Fairfield 2.23  10 0 5.10  10 0 1.72  10 0 0 0 7 6 6 Greenwood 1.09  10 0 1.02  10 0 9.89  10 0 0 0 7 7 6 5 Kershaw 1.83  10 1.68  10 1.02  10 0 4.30  10 0 0 0 6 6 6 6 Lancaster 0 0 0 0 8.26  10 1.44  10 5.10  10 1.72  10 6 5 6 Laurens 0 0 0 0 0 4.78  10 4.80  10 4.30  10 8 8 7 6 4 Lexington 1.23  10 1.06  10 1.33  10 0 4.30  10 0 6.00  10 0 5 5 Newberry 8.60  10 0 0 0 8.60  10 0 0 0 7 7 7 6 Richland 9.40  10 7.58  10 1.17  10 0 6.45  10 0 0 0 5 5 Saluda 8.60  10 0 0 0 8.60  10 0 0 0 7 5 6 7 York 8.40  10 4.80  10 3.57  10 0 8.00  10 0 0 0 8 8 7 8 4 (Midlands) 3.92  10 2.29  10 4.18  10 0 1.21  10 0 6.00  10 0 6 5 6 Chesterfield 3.54  10 4.80  10 3.06  10 0 0 0 0 0 5 5 5 Clarendon 9.90  10 4.80  10 5.10  10 0 0 0 0 0 6 6 6 Darlington 5.49  10 1.92  10 3.57  10 0 0 0 0 0 6 6 Dillon 1.44  10 1.44  10 0 0 0 0 0 0 6 5 6 Florence 4.02  10 9.60  10 3.06  10 0 0 0 0 0 7 6 6 7 5 Georgetown 2.81  10 8.64  10 7.65  10 0 1.12  10 0 6.00  10 0 8 7 7 7 7 Horry 0 0 0 1.08  10 3.02  10 1.63  10 4.69  10 1.42  10 5 5 Lee 9.60  10 9.60  10 0 0 0 0 0 0 Marion 0 0 0 0 0 0 0 0 5 5 Marlboro 5.10  10 0 5.10  10 0 0 0 0 0 6 6 Sumter 0 0 0 0 0 0 1.53  10 1.53  10 5 5 5 Williamsburg 0 0 0 0 0 9.90  10 4.80  10 5.10  10 8 7 7 7 7 (Pee Dee) 1.55  10 4.56  10 3.67  10 0 5.81  10 0 1.48  10 0 Allendale 0 0 0 0 0 0 0 0 Bamberg 0 0 0 0 0 0 0 0 6 6 Barnwell 1.44  10 1.44  10 0 0 0 0 0 0 7 7 6 6 6 6 Beaufort 6.37  10 4.66  10 4.08  10 0 3.87  10 3.45  10 5.70  10 0 7 6 6 7 5 Berkeley 0 0 0 2.42  10 6.24  10 1.53  10 1.55  10 9.60  10 5 5 5 Calhoun 0 0 0 0 0 9.90  10 4.80  10 5.10  10 8 7 7 7 6 Charleston 1.20  10 2.64  10 1.07  10 0 8.08  10 0 1.92  10 0 6 5 5 5 Colleton 1.89  10 9.60  10 5.10  10 0 0 0 4.20  10 0 7 6 7 4 Dorchester 3.94  10 6.24  10 0 0 3.31  10 0 6.00  10 0 Hampton 0 0 0 0 0 0 0 0 7 6 6 6 5 Jasper 1.35  10 4.32  10 0 0 7.74  10 1.15  10 3.00  10 0 5 5 McCormick 8.60  10 0 0 0 8.60  10 0 0 0 5 5 5 Orangeburg 9.10  10 4.80  10 0 0 4.30  10 0 0 0 8 7 7 8 6 6 (Low Country) 2.67  10 9.31  10 1.73  10 0 1.42  10 4.60  10 9.36  10 0 8 8 8 8 6 7 Totals 8.53  10 3.84  10 1.14  10 0 3.27  10 4.60  10 2.42  10 0 Earth 2021, 2 686 Table 13. The total dollar value of mid-point soil inorganic carbon (SIC) storage value for newly “developed” land covers (open space, low, medium, and high intensity) from 2001 to 2016 in the state of South Carolina (U.S.A.) based on a social cost of carbon (SC-CO ) of $46 per metric ton of CO (2007 U.S. dollars with an average discount rate of 3% [16]). 2 2 Degree of Weathering and Soil Development County Total (Region) SC-CO 2 Slightly Weathered Moderately Weathered Strongly Weathered ($) Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols SC-CO ($) 5 5 Anderson 4.10  10 4.10  10 0 0 0 0 0 0 5 5 5 4 Cherokee 7.36  10 4.92  10 1.72  10 0 7.20  10 0 0 0 6 5 6 Greenville 0 0 0 0 0 2.27  10 9.84  10 1.29  10 5 5 5 Oconee 0 0 0 0 0 4.18  10 2.46  10 1.72  10 5 5 4 Pickens 2.50  10 1.64  10 8.60  10 0 0 0 0 0 6 5 6 5 Spartanburg 2.06  10 3.28  10 1.38  10 0 3.60  10 0 0 0 5 4 5 Union 5.86  10 8.20  10 0 0 5.04  10 0 0 0 6 6 6 5 (Upstate) 6.74  10 2.71  10 3.10  10 0 9.36  10 0 0 0 5 5 Abbeville 0 0 0 0 0 0 4.32  10 4.32  10 6 6 5 5 Aiken 5.15  10 4.26  10 6.02  10 0 2.88  10 0 0 0 6 4 5 Chester 1.02  10 0 8.60  10 0 9.36  10 0 0 0 5 5 5 4 Edgefield 9.04  10 5.74  10 2.58  10 0 7.20  10 0 0 0 5 4 5 Fairfield 3.74  10 0 8.60  10 0 2.88  10 0 0 0 6 5 6 Greenwood 1.83  10 0 1.72  10 0 1.66  10 0 0 0 6 6 5 4 Kershaw 3.11  10 2.87  10 1.72  10 0 7.20  10 0 0 0 6 5 5 5 Lancaster 0 0 0 0 1.39  10 2.46  10 8.60  10 2.88  10 5 4 5 Laurens 0 0 0 0 0 8.02  10 8.20  10 7.20  10 7 7 6 5 4 Lexington 2.10  10 1.80  10 2.24  10 0 7.20  10 0 1.00  10 0 5 5 Newberry 1.44  10 0 0 0 1.44  10 0 0 0 7 7 6 6 Richland 1.60  10 1.30  10 1.98  10 0 1.08  10 0 0 0 5 5 Saluda 1.44  10 0 0 0 1.44  10 0 0 0 7 4 5 7 York 1.41  10 8.20  10 6.02  10 0 1.34  10 0 0 0 7 7 6 7 4 (Midlands) 6.64  10 3.91  10 7.05  10 0 2.02  10 0 1.00  10 0 5 4 5 Chesterfield 5.98  10 8.20  10 5.16  10 0 0 0 0 0 5 4 4 Clarendon 1.68  10 8.20  10 8.60  10 0 0 0 0 0 5 5 5 Darlington 9.30  10 3.28  10 6.02  10 0 0 0 0 0 5 5 Dillon 2.46  10 2.46  10 0 0 0 0 0 0 5 5 5 Florence 6.80  10 1.64  10 5.16  10 0 0 0 0 0 6 6 6 6 5 Georgetown 4.74  10 1.48  10 1.29  10 0 1.87  10 0 1.00  10 0 7 6 6 6 6 Horry 0 0 0 1.81  10 5.17  10 2.75  10 7.85  10 2.36  10 5 5 Lee 1.64  10 1.64  10 0 0 0 0 0 0 Marion 0 0 0 0 0 0 0 0 4 4 Marlboro 8.60  10 0 8.60  10 0 0 0 0 0 5 5 Sumter 0 0 0 0 0 0 2.58  10 2.58  10 5 4 4 Williamsburg 0 0 0 0 0 1.68  10 8.20  10 8.60  10 7 6 6 6 6 (Pee Dee) 2.62  10 7.79  10 6.19  10 0 9.72  10 0 2.46  10 0 Allendale 0 0 0 0 0 0 0 0 Bamberg 0 0 0 0 0 0 0 0 5 5 Barnwell 2.46  10 2.46  10 0 0 0 0 0 0 7 6 5 5 5 5 Beaufort 1.08  10 7.95  10 6.88  10 0 6.48  10 5.79  10 9.50  10 0 6 6 5 6 5 Berkeley 0 0 0 4.08  10 1.07  10 2.58  10 2.59  10 1.60  10 5 4 4 Calhoun 0 0 0 0 0 1.68  10 8.20  10 8.60  10 7 6 6 7 5 Charleston 2.02  10 4.51  10 1.81  10 0 1.35  10 0 3.20  10 0 5 5 4 4 Colleton 3.20  10 1.64  10 8.60  10 0 0 0 7.00  10 0 6 6 6 4 Dorchester 6.62  10 1.07  10 0 0 5.54  10 0 1.00  10 0 Hampton 0 0 0 0 0 0 0 0 6 5 6 5 4 Jasper 2.28  10 7.38  10 0 0 1.30  10 1.93  10 5.00  10 0 5 5 McCormick 1.44  10 0 0 0 1.44  10 0 0 0 5 4 4 Orangeburg 1.54  10 8.20  10 0 0 7.20  10 0 0 0 7 7 6 7 5 6 (Low Country) 4.50  10 1.59  10 2.92  10 0 2.38  10 7.72  10 1.56  10 0 8 7 7 7 5 6 Totals 1.44  10 6.55  10 1.93  10 0 5.47  10 7.72  10 4.03  10 0 Earth 2021, 2 687 Table 14. Mid-point total soil carbon (TSC) storage for newly “developed” land covers (open space, low, medium, and high intensity) from 2001 to 2016 in the state of South Carolina (U.S.A.) based on mid-point SIC content numbers in the upper 2 m of the soil based on data from Guo et al., 2006 [17]. Degree of Weathering and Soil Development County Total TSC Slightly Weathered Moderately Weathered Strongly Weathered (Region) (kg) Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols TSC (kg) 8 6 8 Anderson 2.16  10 6.40  10 0 0 0 0 0 2.09  10 7 6 6 6 7 Cherokee 5.71  10 7.68  10 2.80  10 0 1.18  10 0 0 4.54  10 8 7 7 8 Greenville 0 0 0 0 4.82  10 1.54  10 2.10  10 4.45  10 8 6 6 7 Oconee 0 0 0 0 1.05  10 3.84  10 2.80  10 9.87  10 7 6 6 7 Pickens 8.99  10 2.56  10 1.40  10 0 0 0 0 8.59  10 8 6 7 6 8 Spartanburg 2.96  10 5.12  10 2.24  10 0 5.90  10 0 0 2.63  10 7 6 6 7 Union 3.37  10 1.28  10 0 0 8.26  10 0 0 2.41  10 9 7 7 7 9 (Upstate) 1.28  10 4.22  10 5.04  10 0 1.53  10 0 0 1.17  10 7 6 7 Abbeville 0 0 0 0 0 2.55  10 7.08  10 1.85  10 8 7 6 6 8 Aiken 2.54  10 6.66  10 9.80  10 0 4.72  10 0 0 1.73  10 7 6 7 7 Chester 3.31  10 0 1.40  10 0 1.53  10 0 0 1.63  10 7 6 6 6 7 Edgefield 4.98  10 8.96  10 4.20  10 0 1.18  10 0 0 3.55  10 7 6 6 7 Fairfield 3.24  10 0 1.40  10 0 4.72  10 0 0 2.63  10 7 6 7 7 Greenwood 7.04  10 0 2.80  10 0 2.71  10 0 0 4.05  10 8 7 6 6 7 Kershaw 1.06  10 4.48  10 2.80  10 0 1.18  10 0 0 5.75  10 8 6 7 6 8 Lancaster 0 0 0 1.58  10 3.84  10 1.40  10 4.72  10 1.36  10 7 6 7 7 Laurens 0 0 0 0 6.85  10 1.28  10 1.18  10 5.54  10 8 8 7 7 6 8 Lexington 6.13  10 2.82  10 3.64  10 0 1.18  10 0 1.29  10 2.82  10 7 6 7 Newberry 3.36  10 0 0 0 2.36  10 0 0 3.12  10 8 8 7 7 8 Richland 5.63  10 2.02  10 3.22  10 0 1.77  10 0 0 3.11  10 7 6 7 Saluda 2.30  10 0 0 0 2.36  10 0 0 2.06  10 8 6 6 8 8 York 4.59  10 1.28  10 9.80  10 0 2.19  10 0 0 2.29  10 9 8 8 8 6 9 (Midlands) 2.49  10 6.11  10 1.15  10 0 3.32  10 0 1.29  10 1.43  10 7 6 6 7 Chesterfield 2.53  10 1.28  10 8.40  10 0 0 0 0 1.56  10 7 6 6 7 Clarendon 2.68  10 1.28  10 1.40  10 0 0 0 0 2.41  10 7 6 6 7 Darlington 5.97  10 5.12  10 9.80  10 0 0 0 0 4.47  10 7 6 7 Dillon 1.73  10 3.84  10 0 0 0 0 0 1.35  10 8 6 6 8 Florence 1.45  10 2.56  10 8.40  10 0 0 0 0 1.34  10 8 7 7 7 7 7 Georgetown 1.08  10 2.30  10 2.10  10 0 3.07  10 0 1.29  10 1.99  10 8 7 7 8 8 8 Horry 0 0 8.43  10 8.06  10 4.48  10 1.29  10 3.04  10 2.85  10 7 6 6 Lee 1.18  10 2.56  10 0 0 0 0 0 9.23  10 7 7 Marion 1.07  10 0 0 0 0 0 0 1.07  10 7 6 7 Marlboro 1.21  10 0 1.40  10 0 0 0 0 1.07  10 8 6 8 Sumter 1.11  10 0 4.20  10 0 0 0 0 1.07  10 7 6 6 7 Williamsburg 1.48  10 1.28  10 1.40  10 0 0 0 0 1.21  10 9 8 8 8 8 8 (Pee Dee) 1.39  10 1.22  10 1.01  10 0 1.59  10 0 3.17  10 6.87  10 6 6 Allendale 6.39  10 0 0 0 0 0 0 6.39  10 6 6 Bamberg 3.55  10 0 0 0 0 0 0 3.55  10 7 6 7 Barnwell 1.73  10 3.84  10 0 0 0 0 0 1.35  10 8 8 7 7 6 8 8 Beaufort 3.80  10 1.24  10 1.12  10 0 1.06  10 7.50  10 1.23  10 1.04  10 8 7 6 7 7 8 Berkeley 3.01  10 1.66  10 4.20  10 0 4.25  10 0 2.06  10 2.17  10 7 6 6 7 Calhoun 1.40  10 1.28  10 1.40  10 0 0 0 0 1.14  10 8 7 7 8 7 7 Charleston 0 0 4.19  10 7.04  10 2.94  10 2.22  10 4.13  10 5.61  10 7 6 6 6 6 Colleton 0 0 0 2.01  10 2.56  10 1.40  10 9.03  10 7.10  10 8 7 7 6 7 Dorchester 1.92  10 1.66  10 0 0 9.09  10 0 1.29  10 8.31  10 6 6 Hampton 3.55  10 0 0 0 0 0 0 3.55  10 8 7 7 6 6 7 Jasper 1.04  10 1.15  10 0 0 2.12  10 2.50  10 6.45  10 6.25  10 7 6 6 McCormick 1.02  10 0 0 0 2.36  10 0 0 7.81  10 7 6 6 7 Orangeburg 4.22  10 1.28  10 0 0 1.18  10 0 0 3.98  10 9 8 7 8 7 8 8 (Low Country) 1.51  10 2.48  10 4.76  10 0 3.91  10 1.00  10 2.01  10 6.16  10 9 9 8 8 7 8 9 Totals 6.67  10 1.02  10 3.14  10 0 8.97  10 1.00  10 5.20  10 3.91  10 Earth 2021, 2 688 Table 15. The total dollar value of mid-point total soil carbon (TSC) storage value for newly “developed” land covers (open space, low, medium, and high intensity) from 2001 to 2016 in the state of South Carolina (U.S.A.) based on a social cost of carbon (SC-CO ) of $46 per metric ton of CO (2007 U.S. dollars with an average discount rate of 3% [16]). 2 2 Degree of Weathering and Soil Development Total County SC-CO 2 Slightly Weathered Moderately Weathered Strongly Weathered (Region) ($) Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols SC-CO ($) 7 6 7 Anderson 3.65  10 1.09  10 0 0 0 0 0 3.54  10 6 6 5 5 6 Cherokee 9.65  10 1.30  10 4.72  10 0 1.99  10 0 0 7.68  10 7 6 6 7 Greenville 0 0 0 0 8.14  10 2.60  10 3.54  10 7.52  10 7 5 5 7 Oconee 0 0 0 0 1.78  10 6.51  10 4.72  10 1.67  10 7 5 5 7 Pickens 1.52  10 4.34  10 2.36  10 0 0 0 0 1.45  10 7 5 6 5 7 Spartanburg 5.00  10 8.68  10 3.78  10 0 9.95  10 0 0 4.44  10 6 5 6 6 Union 5.69  10 2.17  10 0 0 1.39  10 0 0 4.08  10 8 6 6 6 8 (Upstate) 2.16  10 7.16  10 8.50  10 0 2.59  10 0 0 1.98  10 6 6 6 Abbeville 0 0 0 0 0 4.31  10 1.19  10 3.12  10 7 7 6 5 7 Aiken 4.30  10 1.13  10 1.65  10 0 7.96  10 0 0 2.93  10 6 5 6 6 Chester 5.58  10 0 2.36  10 0 2.59  10 0 0 2.76  10 6 6 5 5 6 Edgefield 8.43  10 1.52  10 7.08  10 0 1.99  10 0 0 6.00  10 6 5 5 6 Fairfield 5.47  10 0 2.36  10 0 7.96  10 0 0 4.44  10 7 5 6 6 Greenwood 1.19  10 0 4.72  10 0 4.58  10 0 0 6.84  10 7 6 5 5 6 Kershaw 1.80  10 7.60  10 4.72  10 0 1.99  10 0 0 9.72  10 7 5 6 5 7 Lancaster 0 0 0 2.67  10 6.51  10 2.36  10 7.96  10 2.29  10 7 5 6 6 Laurens 0 0 0 0 1.16  10 2.17  10 1.99  10 9.36  10 8 7 6 6 5 7 Lexington 1.04  10 4.77  10 6.14  10 0 1.99  10 0 2.17  10 4.76  10 6 5 6 Newberry 5.68  10 0 0 0 3.98  10 0 0 5.28  10 7 7 6 6 7 Richland 9.53  10 3.43  10 5.43  10 0 2.99  10 0 0 5.26  10 6 5 6 Saluda 3.88  10 0 0 0 3.98  10 0 0 3.48  10 7 5 6 7 7 York 7.75  10 2.17  10 1.65  10 0 3.70  10 0 0 3.86  10 8 8 7 7 5 8 (Midlands) 4.21  10 1.04  10 1.94  10 0 5.59  10 0 2.17  10 2.42  10 6 5 6 6 Chesterfield 4.27  10 2.17  10 1.42  10 0 0 0 0 2.64  10 6 5 5 6 Clarendon 4.53  10 2.17  10 2.36  10 0 0 0 0 4.08  10 7 5 6 6 Darlington 1.01  10 8.68  10 1.65  10 0 0 0 0 7.56  10 6 5 6 Dillon 2.93  10 6.51  10 0 0 0 0 0 2.28  10 7 5 6 7 Florence 2.45  10 4.34  10 1.42  10 0 0 0 0 2.27  10 7 6 6 6 6 6 Georgetown 1.82  10 3.91  10 3.54  10 0 5.17  10 0 2.17  10 3.36  10 8 7 6 7 7 7 Horry 0 0 1.42  10 1.37  10 7.55  10 2.17  10 5.12  10 4.81  10 6 5 6 Lee 1.99  10 4.34  10 0 0 0 0 0 1.56  10 6 6 Marion 1.80  10 0 0 0 0 0 0 1.80  10 6 5 6 Marlboro 2.04  10 0 2.36  10 0 0 0 0 1.80  10 7 5 7 Sumter 1.88  10 0 7.08  10 0 0 0 0 1.81  10 6 5 5 6 Williamsburg 2.49  10 2.17  10 2.36  10 0 0 0 0 2.04  10 8 7 7 7 7 8 (Pee Dee) 2.34  10 2.06  10 1.70  10 0 2.69  10 0 5.34  10 1.16  10 6 6 Allendale 1.08  10 0 0 0 0 0 0 1.08  10 5 5 Bamberg 6.00  10 0 0 0 0 0 0 6.00  10 6 5 6 Barnwell 2.93  10 6.51  10 0 0 0 0 0 2.28  10 7 7 6 6 6 7 7 Beaufort 6.41  10 2.10  10 1.89  10 0 1.79  10 1.26  10 2.06  10 1.75  10 7 6 5 6 6 7 Berkeley 5.09  10 2.82  10 7.08  10 0 7.16  10 0 3.47  10 3.67  10 6 5 5 6 Calhoun 2.37  10 2.17  10 2.36  10 0 0 0 0 1.92  10 7 7 6 7 6 6 Charleston 0 0 7.07  10 1.19  10 4.96  10 3.74  10 6.94  10 9.48  10 6 5 5 6 6 Colleton 3.39  10 4.34  10 2.36  10 0 0 0 1.52  10 1.20  10 7 6 7 5 7 Dorchester 3.24  10 2.82  10 0 0 1.53  10 0 2.17  10 1.40  10 5 5 Hampton 6.00  10 0 0 0 0 0 0 6.00  10 7 6 6 5 6 7 Jasper 1.76  10 1.95  10 0 0 3.58  10 4.21  10 1.09  10 1.06  10 6 5 6 McCormick 1.72  10 0 0 0 3.98  10 0 0 1.32  10 6 5 5 6 Orangeburg 7.14  10 2.17  10 0 0 1.99  10 0 0 6.72  10 8 7 6 7 6 7 8 (Low Country) 2.56  10 4.21  10 8.02  10 0 6.59  10 1.68  10 3.39  10 1.04  10 9 8 7 8 6 7 8 Totals 1.13  10 1.73  10 5.29  10 0 1.51  10 1.68  10 8.75  10 6.60  10 Earth 2021, 2 689 4. Discussion 4.1. Determining “Hotspots” of Social Costs of C from Land Cover Change and Pedodiversity Nationally, patterns of LULC are changing rapidly due to urbanization and agricul- tural expansion [9], with regional [24], state [25], and county [12] differences. Increasingly LULC change analysis is conducted in conjunction with the ES framework [12], with lim- ited applications of soil ES/ED. The Southeastern United States and South Carolina are experiencing some of the highest urbanization rates and losses of ecosystem services [12,22]. Mikhailova et al. (2020) [15] examined soil C regulating ES in the state of South Carolina and reported the value of the social cost of carbon dioxide (SC-CO ) emissions by soil C type (SOC, SIC, TSC), soil order, region, and county in the context of “avoided” versus potential “realized” social costs of carbon. Although informative, that study lacked inte- gration with LULC change analysis to indicate “hotspots” of potential “realized” social costs of C because of disturbance (change in LULC), especially in the “developed” LULC category. The present study integrates soil information with LULC change, which provides detailed information about SC-CO by LULC and soil type, as well as the LULC change by soil type. It is especially important because different soil types have different spatial distributions within the state and even association with a particular LULC (e.g., Histosols are commonly found in woody wetlands; emergent herbaceous wetlands), which can make them particularly sensitive to LULC changes and corresponding social costs. This study demonstrates that most of the soils have experienced losses in “low disturbance” LULC (e.g., woody wetlands, herbaceous) and gains in open, low, medium, and high intensity developed land cover classes with $1.1B in most likely “realized” social cost of C primarily associated with Ultisols ($658.8M). Ultisols are highly leached and highly erodible soils that occupy 70% of the area of South Carolina. It should be noted that both the state of South Carolina and Ultisols in particular, historically, experienced tremendous social costs associated with agricultural production in the form of high soil erosion [26], carbon loss, depletion of soil nutrients, and even “socioecological collapse that only state-initiated intervention” could address [27]. There is a legacy of various social costs associated with land and soil degradation in the state of South Carolina [27], which are only now being examined within the framework of ES/ED [12,15]. This legacy demonstrates a common trend with other places in the world, which involves maximizing provisioning ES at the expense of regulating ES and resulting in high social costs at the expense of environment and ordinary people [28,29]. The advantage of the present study is that it uses satellite-based remote sensing to accurately identify land cover and the areas with changing land cover [30], which is important because the land cover and land cover change are likely directly related to the level of disturbance [31]. Land cover maps derived from satellites allow the spatially explicit tracking of land cover and land cover change [32,33]. Many studies that examine the relationship of land cover change to ES rely on fixed estimates for soil carbon that are assumed for each land cover category, instead of relying on estimates from existing soil databases [12]. This study combines the spatial information from land cover maps with soil carbon estimates from soil databases to identify hotspots of disturbance (Figure 4). Because land cover maps are regularly updated [32], it is possible to use this framework to identify and monitor likely SC-CO hotspots over time and calculate cumulative costs associated with land cover change. Future improvements to the study methodology would be to include actual field samples, which would be more accurate than soil order averages; however, the soil carbon content trends are likely the same between the different soil types [11]. Linking this type of study to land cover change models would help inform decision-makers about potential impacts caused by land development [28]. With the existing database of satellite images for at least the last 49 years [34], it will be possible to track land cover change and disturbance over time to estimate the impact of development and understand the future trajectory of disturbance. Earth 2021, 2, FOR PEER REVIEW 17 Earth 2021, 2 690 Figure 4. The total dollar value of mid-point total soil carbon (TSC) storage value for newly “devel- Figure 4. The total dollar value of mid-point total soil carbon (TSC) storage value for newly “de- veloped” oped” land land covers covers (open space, (open space, low low, medium, , medium, and and high intensity) from high intensity) from 2001 2001 to 2016 to 2016 in in the the state state of of South Carolina (U.S.A.) based on a social cost of carbon (SC-CO2) of $46 per metric ton of CO2 South Carolina (U.S.A.) based on a social cost of carbon (SC-CO ) of $46 per metric ton of CO (2007 2 2 (2007 U.S. dollars with an average discount rate of 3% [17]). U.S. dollars with an average discount rate of 3% [16]). 4.2. Determining Potential for C Sequestration Using LULC and Pedodiversity 4.2. Determining Potential for C Sequestration using LULC and Pedodiversity Recarbonization potential is dependent on both LULC and pedodiversity, which Recarbonization potential is dependent on both LULC and pedodiversity, which should be considered together for sustainable carbon management at various administra- should be considered together for sustainable carbon management at various administra- tive levels (e.g., state, region, county, etc.) (Table 16). For example, Table 16 demonstrates tive levels (e.g., state, region, county, etc.) (Table 16). For example, Table 16 demonstrates LULC by soil order at the state level, which shows the inherent soil capacity (based on soil LULC by soil order at the state level, which shows the inherent soil capacity (based on soil properties) for C sequestration and LULC covers (either natural, managed, or in combina- properties) for C sequestration and LULC covers (either natural, managed, or in combi- tion). According to Table 16, most LULC in the state of South Carolina are dominated by nation). According to Table 16, most LULC in the state of South Carolina are dominated the soil order of Ultisols, which have a low recarbonization potential because these soils by the soil order of Ultisols, which have a low recarbonization potential because these are highly weathered with low nutrient holding capacity [35]. Cherkinsky et al. (2018) [36] soils are highly weathered with low nutrient holding capacity [35]. Cherkinsky et al. reported that land use has remarkable effects on Ultisols and forest root systems at great (2018) [36] reported that land use has remarkable effects on Ultisols and forest root sys- depth, which can result in “sluggish regeneration rates of forest root systems” compared to tems at great depth, which can result in “sluggish regeneration rates of forest root sys- aboveground regeneration. tems” compared to aboveground regeneration. Land covers and their changes are often associated with “human-dimension is- Land covers and their changes are often associated with “human-dimension issues” sues” [37], which have a direct impact on soil C. Clay et al. (2019) [38] reported that [37], which have a direct impact on soil C. Clay et al. (2019) [38] reported that there is a there is a significant potential for South Carolina forests to sequester C in the aboveground significant potential for South Carolina forests to sequester C in the aboveground biomass, biomass, but soil C sequestration potential would be limited based on other studies for soils but soil C sequestration potential would be limited based on other studies for soils in the in the state [35,36]. Because most of the South Carolina forestland is in private ownership, state [35,36]. Because most of the South Carolina forestland is in private ownership, forest forest landowners play an important role in decision making with regards to potential landowners play an important role in decision making with regards to potential forest forest management practices aimed at C sequestration both in aboveground biomass and management practices aimed at C sequestration both in aboveground biomass and in the in the soil [39]. According to Richter et al. (1999) [40], soil C losses because of deforestation soil [39]. According to Richter et al. (1999) [40], soil C losses because of deforestation and and cultivation are well-documented, but studies on “soil-carbon recovery after cultivation cultivation are well-documented, but studies on “soil-carbon recovery after cultivation are limited”. Richter et al. (1999) [40] used data from a four-decade-long field study of are limited.” Richter et al. (1999) [40] used data from a four-decade-long field study of C C accumulation by pine ecosystems grown on previously cultivated soils at the Calhoun accumulation by pine ecosystems grown on previously cultivated soils at the Calhoun Experimental Forest in South Carolina (USA) and concluded that “despite high C inputs to Experimental Forest in South Carolina (USA) and concluded that “despite high C inputs the mineral soil, C sequestration was limited by rapid decomposition, facilitated by the to the mineral soil, C sequestration was limited by rapid decomposition, facilitated by the coarse soil texture and low-activity clay mineralogy”. coarse soil texture and low-activity clay mineralogy.” Earth 2021, 2 691 Table 16. Land use/land cover (LULC) by soil order in the state of South Carolina (USA) in 2016. Degree of Weathering and Soil Development 2016 Total Slight Moderate Strong NLCD Land Cover Classes Area by LULC Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols (LULC) (km ) (%) 2016 Area by Soil Order (% from Total Area in Each LULC) Barren land 162.4 (0.2%) 28.6 4.2 0.1 7.0 0.1 2.2 57.9 Woody wetlands 15,769.9 (20.7%) 9.4 25.1 1.9 8.5 1.1 2.7 51.3 Shrub/Scrub 2702.8 (3.5%) 10.1 4.0 0.1 8.7 0.0 1.2 75.8 Mixed forest 4717.3 (6.2%) 5.2 8.3 0.0 14.1 0.1 0.3 72.0 Deciduous forest 7231.9 (9.5%) 6.3 8.5 0.0 11.6 0.1 0.2 73.4 Herbaceous 3250.8 (4.3%) 12.0 4.5 0.2 8.0 0.0 1.0 74.3 Evergreen forest 19,637.6 (25.7%) 5.9 4.3 0.1 12.5 0.1 2.4 74.6 Emergent herbaceous wetlands 2037.1 (2.7%) 74.1 4.7 6.1 2.2 0.4 0.9 11.6 Hay/Pasture 5415.8 (7.1%) 4.4 3.3 0.0 8.0 0.0 0.5 83.8 Cultivated crops 7452.5 (9.8%) 3.6 3.3 0.2 1.1 0.0 1.0 90.8 Developed, open space 4914.0 (6.4%) 7.4 4.0 0.1 7.0 0.1 3.1 78.3 Developed, medium intensity 673.2 (0.9%) 12.3 2.8 0.0 8.0 0.0 4.0 73.0 Developed, low intensity 2080.8 (2.7%) 10.1 3.2 0.0 7.6 0.0 3.1 75.9 Developed, high intensity 225.3 (0.3%) 10.3 2.4 0.0 8.0 0.0 2.7 76.6 4.3. Significance of Results for South Carolina At present, state of South Carolina has no comprehensive state climate strategy [41]. However, in 2008 a special task force appointed by the South Carolina governor recom- mended more than 50 ways to stop rising greenhouse gas pollution from worsening global warming [42], which included policy recommendations for various economic sectors (e.g., tourism and recreation; agriculture and forestry, etc.). Our study here contributes impor- tant scientific findings and information that supports the final report of the special task force [42] in the following ways: Inventory of South Carolina’s Greenhouse Gas Emissions. Current SC GHG inven- tories do not include soil and LULC analysis. This study quantified and valued the soil C regulating ES for SOC, SIC, and TSC for the state of South Carolina by soil type and LULC. According to the final report by the South Carolina Climate, Energy, and Commerce Committee [42]: “South Carolina’s gross emissions of GHGs grew by 39% between 1990 and 2005, twice the national average of 16%. South Carolina’s emissions growth was driven by the growth of its population and many other factors. In addition, the state’s emissions on a per-capita basis increased by about 15% between 1990 and 2005, while U.S. per-capita emissions declined slightly (2%) over this period due to many other factors. South Carolina’s gross GHG emissions are projected to rise fairly steeply to about 125 MMtCO2e by 2025, or 87% over 1990 levels”. Areas that developed in South Carolina over a 15-year time period were evaluated by the combination of soil type, LULC, and county to calculate the potential realized SC-CO from this land conversion. By identifying these potential SC-CO hotspots, future land conversions could be reduced to help lower future greenhouse gas emissions. Agriculture, Forestry and Waste Management (AFW): AFW-6a: Terrestrial Carbon Sequestration—Agriculture. This study provides spa- tial information about soil C stocks by soil type, LULC (including agricultural LULC), and associated SC-CO over time, which can be used to create a database to monitor the effectiveness of C sequestration using a cost-benefit analysis. AFW-6bi: Terrestrial Carbon Sequestration—Forestry: Forest Management. This study provides spatial information about soil C stocks by soil type, LULC (including different forest LULC), and associated SC-CO over time, which can be used to create a database to monitor the effectiveness of C sequestration using a cost-benefit analysis. AFW-6bii: Terrestrial Carbon Sequestration—Forestry: Afforestation/Reforestation. This study provides spatial information about soil C stocks by soil type, LULC (including different forest LULC) and associated SC-CO over time, which can be used to create a database to monitor the effectiveness of C sequestration using a cost-benefit analysis. Earth 2021, 2 692 AFW-6biii: Terrestrial Carbon Sequestration—Forestry: Urban Forestry. This study provides spatial information about soil C stocks by soil type, LULC (including different forest, developed LULC), and associated SC-CO over time, which can be used to create a database to monitor the effectiveness of C sequestration using a cost-benefit analysis. AFW-7a: Conservation and Restoration of Agriculture Lands for Enhanced Carbon Sequestration. This study provides information about soil C stocks by soil type, LULC (including agricultural LULC), and associated SC-CO over time, which can be used to create a database to monitor the effectiveness of C sequestration using a cost-benefit analysis. AFW-7b: Conservation and Restoration of Forestlands for Enhanced Carbon Se- questration. This study provides information about soil C stocks by soil type, LULC (including different forest LULC), and associated SC-CO over time, which can be used to create a database to monitor the effectiveness of C sequestration using a cost-benefit analysis. Transportation and Land Use (TLU): TLU-4: Improve Development Patterns. This study quantifies SC-CO as “avoided” and “realized” cost, which can be used for cost-benefit analysis when evaluating the development plans. Cross-Cutting (CC) Issues: CC-1: Inventories and Forecasting. This study provides spatial information about soil C stocks by soil type, LULC, and associated SC-CO over time, which can be used in already existing inventories and forecasting efforts. CC-2: GHG Reporting and Registry. This study reported spatial information about soil C stocks by soil type, LULC, and associated SC-CO over time, which can be used in GHG reporting and registry. CC-3: State Government GHG Emissions. This study reported spatial information about soil C stocks by soil type, LULC, and associated SC-CO over time at the state level. CC-4: Comprehensive Local Government Climate Actions Plans (Counties, Cities, etc.). This study reported spatial information about soil C stocks by soil type, LULC, and associated SC-CO overtime at the county level. CC-5: Public Education and Outreach. Results from this study can be used for vari- ous educational (e.g., soil science courses, textbooks, etc.) and extension efforts (e.g., infor- mal education not only with the agricultural communities but also with developers, etc.). CC-6: Adaptation and Vulnerability. Spatial information about the SC-CO hotspots can be used to optimize economic opportunity while minimizing environmental impact. 5. Conclusions This study examined the integration of soil diversity (pedodiversity) concepts (tax- onomic) with land cover change analysis to value soil C regulating ES/ED in the state of South Carolina (U.S.A.) to be considered in territorial planning. Both pedodiversity and land cover information provide critical contexts (e.g., “portfolio-effect”, “distribution- effect,” “evenness-effect,” etc.) for analyzing, interpreting, and reporting ES/ED within the ES framework for sustainable management of soil carbon within the state. Soil carbon is either protected in the soil or subject to release to the atmosphere because of disturbance which depends on the land cover and land cover change. The percent change (between 2001 and 2016) both in areas and monetary values varied by soil order and land cover, with most soil orders experiencing losses in “low disturbance” land covers (e.g., woody wetlands, herbaceous) and gains in open, low, medium, and high intensity developed land cover classes with $1.1B in most likely “realized” social cost of carbon with the following distribution by soil order: Entisols: $173.6M, 15% from the total; Inceptisols: $54.3M, 5%; Histosols: $0.0, 0%; Alfisols: $151.2M, 13%; Mollisols: $4.2M, 1%; Spodosols: $91.1M, 8%; Ultisols: $658.8M, 58%). Most of the “realized” social costs of C were associated with Ultisols, a highly weathered soil covering the largest proportion of the state (70%). It is Earth 2021, 2 693 important to consider the social cost of soil C associated with land cover categories as well as their change in value over time (between 2001 and 2016) when determining if the soil carbon is likely stable or at risk. Considering the land covers with the largest social cost of soil C based on area and soil type, such as woody wetlands and evergreen forests, had very little change, 1% and +1% respectively. The total social cost of soil C for deciduous forest was reduced by 7%; similarly, hay/pasture was reduced by 10%, and barren land was reduced by 9%. Cultivated crops remained unchanged, while herbaceous land cover was reduced by 5%, and emergent herbaceous wetlands were reduced by 3%. Mixed forests increased by 5%, and shrub/scrub land increased by 9%. The developed categories increased between 2001 and 2016. Social cost soil C of developed open space increased by 5%, while developed low intensity increased by 15%. The social cost of soil C for developed medium intensity increased by 46% and developed high intensity increased by 39%. The percent change in monetary values was different from the percent change in areas because different soil orders have different TSC contents, which are dependent on the degree of soil weathering and development, with highly weathered soils having lower TSC compared to moderately weathered soils. The Midlands region experienced the highest gains in the “high disturbance” classes and corresponding SC-CO with over $421M for TSC, $354.6M for SOC, and $66.4M for SIC. Among counties, Horry County ranked first with over $142.2M in SC-CO for TSC, followed by Lexington ($103.7M), Rich- land ($95.3M), Greenville ($81.4M), York ($77.5M), Charleston ($70.7M), Beaufort ($64.1M), Berkeley ($50.9M), Spartanburg ($50.0M), and Aiken ($43.0M) counties. Administrative area of the state combined with pedodiversity and land cover change concepts can provide useful information to design cost-efficient policies to manage soil carbon regulating ES at the state level. Results of this study were linked with the proposed policy recommendations by South Carolina’s Climate, Energy, and Commerce Advisory Committee (CECAC). Author Contributions: Conceptualization, E.A.M.; methodology, E.A.M., M.A.S., and L.L.; formal analysis, E.A.M.; writ-ing—original draft preparation, E.A.M.; writing—review and editing, E.A.M., C.J.P., G.C.P., and M.A.S.; visualization, L.L., H.A.Z., and Z.H. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: We would like to thank the reviewers for their constructive comments and suggestions. Conflicts of Interest: The authors declare no conflict of interest. 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South Carolina forestland owner ’s willingness to accept compensations for carbon sequestration. For. Ecosyst. 2019, 6, 16. [CrossRef] 40. Richter, D.D.; Markewitz, D.; Trumbore, S.E.; Wells, C.G. Rapid accumulation and turnover of soil carbon in a re-establishing forest. Nature 1999, 400, 56–58. [CrossRef] 41. Fretwell, S. As Heat Rises, SC Watches Quitely. Will State Suffer from Lack of Climate Action? The State 2020. Available online: https://www.thestate.com/news/local/environment/article239527278.html (accessed on 25 August 2021). 42. South Carolina Climate, Energy, and Commerce Committee. Final Report. July 2008. Available online: http://uccrnna.org/ wp-content/uploads/2017/06/South-Carolina_2008_Climate-Energy-Commerce-Committee-Final-Report.pdf (accessed on 25 August 2021). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Earth Multidisciplinary Digital Publishing Institute

Land Cover Change and Soil Carbon Regulating Ecosystem Services in the State of South Carolina, USA

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Article Land Cover Change and Soil Carbon Regulating Ecosystem Services in the State of South Carolina, USA 1 , 2 2 3 1 Elena A. Mikhailova * , Lili Lin , Zhenbang Hao , Hamdi A. Zurqani , Christopher J. Post , 4 5 Mark A. Schlautman and Gregory C. Post Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC 29634, USA; cpost@clemson.edu University Key Lab for Geomatics Technology and Optimized Resources Utilization, No. 15 Shangxiadian Road, Fuzhou 350002, China; lililin@fafu.edu.cn (L.L.); zhenbanghao@fafu.edu.cn (Z.H.) College of Forestry, Agriculture, and Natural Resources, University of Arkansas at Monticello, Monticello, AR 71655, USA; Zurqani@uamont.edu Department of Environmental Engineering and Earth Sciences, Clemson University, Anderson, SC 29625, USA; mschlau@clemson.edu Geography Department, Portland State University, Portland, OR 97202, USA; grpost@pdx.edu * Correspondence: eleanam@clemson.edu Abstract: Integration of land cover change with soil information is important for valuation of soil carbon (C) regulating ecosystem services (ES) and disservices (ED) and for site-specific land management. The objective of this study was to assess the change in value of regulating ES from soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) stocks, based on the concept of the avoided social cost of carbon dioxide (CO ) emissions for the state of South Carolina (SC) in the United States of America (U.S.A.) by soil order (Soil Taxonomy), land cover, and land cover change (National Land Cover Database, NLCD) using information from the State Soil Geographic (STATSGO) and Soil Survey Geographic Database (SSURGO) databases. Classified Citation: Mikhailova, E.A.; Lin, L.; land cover data for 2001 and 2016 were downloaded from the Multi-Resolution Land Characteristics Hao, Z.; Zurqani, H.A.; Post, C.J.; Consortium (MRLC) website. The total estimated monetary mid-point value for TSC in the state Schlautman, M.A.; Post, G.C. Land of South Carolina was $124.42B (i.e., $124.42 billion U.S. dollars, where B = billion = 10 ) with Cover Change and Soil Carbon the following monetary distribution in 2016 and percent change in value between 2001 and 2016: Regulating Ecosystem Services in the barren land ($259.7M, 9%) (i.e., $259.7 million U.S. dollars, where M = million = 10 ), woody State of South Carolina, USA. Earth wetlands ($33.8B, 1%), shrub/scrub ($3.9B, +9%), mixed forest ($6.9B, +5%), deciduous forest 2021, 2, 674–695. https://doi.org/ ($10.6B, 7%), herbaceous ($4.8B, 5%), evergreen forest ($28.6B, +1%), emergent herbaceous 10.3390/earth2040040 wetlands ($6.9B, 3%), hay/pasture ($7.3B, 10%), cultivated crops ($9.9B, 0%), developed, open space ($7.0B, +5%), developed, medium intensity ($978M, +46%), developed, low intensity ($2.9B, Received: 10 June 2021 +15%), and developed, high intensity ($318M, +39%). The percent change in monetary values was Accepted: 29 August 2021 different from percent change in areas because different soil orders have different TSC contents. Published: 26 September 2021 The percent changes (between 2001 and 2016) both in areas and monetary values varied by soil order and land cover with $1.1B in likely “realized” social cost of C mostly associated with Ultisols Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in ($658.8M). The Midlands region of the state experienced the highest gains in the “high disturbance” published maps and institutional affil- classes and corresponding SC-CO with over $421M for TSC, $354.6M for SOC, and $66.4M for SIC. iations. Among counties, Horry County ranked first with over $142.2M in SC-CO for TSC, followed by Lexington ($103.7M), Richland ($95.3M), Greenville ($81.4M), York ($77.5M), Charleston ($70.7M), Beaufort ($64.1M), Berkeley ($50.9M), Spartanburg ($50.0M), and Aiken ($43.0M) counties. Spatial and temporal analyses of land cover can identify critical locations of soil carbon regulating ecosystem Copyright: © 2021 by the authors. services at risk. Licensee MDPI, Basel, Switzerland. This article is an open access article Keywords: accounting; carbon emissions; CO ; inorganic; organic; regulating; soil; urbanization distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Earth 2021, 2, 674–695. https://doi.org/10.3390/earth2040040 https://www.mdpi.com/journal/earth Earth 2021, 2, FOR PEER REVIEW 2 Earth 2021, 2 675 1. Introduction 1. Introduction Ecosystem services (ES) are the benefits people obtain from nature, which fall into Ecosystem services (ES) are the benefits people obtain from nature, which fall into three categories: provisioning (e.g., food, etc.), regulation/maintenance (e.g., gas regula- three categories: provisioning (e.g., food, etc.), regulation/maintenance (e.g., gas regulation, tion, etc.), and cultural (e.g., recreation, etc.) [1,2]. Ecosystem disservices (ED) are dam- etc.), and cultural (e.g., recreation, etc.) [1,2]. Ecosystem disservices (ED) are damages, ages, which can be of natural and/or anthropogenic origin [2]. Soil carbon is composed of which can be of natural and/or anthropogenic origin [2]. Soil carbon is composed of soil soil organic (SOC) and soil inorganic carbon (SIC) and provides numerous ES (e.g., pro- organic (SOC) and soil inorganic carbon (SIC) and provides numerous ES (e.g., provisioning, visioning, regulating) [3–5]. For example, soil C sequestration is a regulating ES, which regulating) [3–5]. For example, soil C sequestration is a regulating ES, which results in the results in the removal of carbon dioxide (CO2) from the atmosphere and subsequent stor- removal of carbon dioxide (CO ) from the atmosphere and subsequent storage in the soil, age in the soil, thereby avoiding social costs of CO2 (SC-CO2) emissions [3,4]. Common ED thereby avoiding social costs of CO (SC-CO ) emissions [3,4]. Common ED associated 2 2 associated with SOC and SIC is the release of carbon dioxide (CO2) from various uses (e.g., with SOC and SIC is the release of carbon dioxide (CO ) from various uses (e.g., agriculture, agriculture, urbanization, etc.) into the atmosphere, which manifests in realized social SC- urbanization, etc.) into the atmosphere, which manifests in realized social SC-CO [6,7]. CO2 [6,7]. Land cover change analysis is particularly useful in ES/ED assessments because Land cover change analysis is particularly useful in ES/ED assessments because it reveals it reveals “patterns of human activities over time and space as well as the capacities of “patterns of human activities over time and space as well as the capacities of different different ecosystems to provide ES/ED under changing land use” [8–10]. ecosystems to provide ES/ED under changing land use” [8–10]. Traditional ES land cover analysis focuses primarily on land cover changes without Traditional ES land cover analysis focuses primarily on land cover changes without integration of specific soil types and their properties [9]. Integration of land cover analysis integration of specific soil types and their properties [9]. Integration of land cover analysis with soil types allows the identification of hotspots of ES/ED and its temporal changes with soil types allows the identification of hotspots of ES/ED and its temporal changes (Figure 1) [11]. For example, Brown and Quinn (2018) [12] studied the ES change in Up- (Figure 1) [11]. For example, Brown and Quinn (2018) [12] studied the ES change in state South Carolina using the InVEST model [13] to estimate SOC change (among other Upstate South Carolina using the InVEST model [13] to estimate SOC change (among variables). The InVEST model assumes fixed soil carbon values associated with land cover other variables). The InVEST model assumes fixed soil carbon values associated with land classes to assess change and does not leverage the available soil spatial data, which could cover classes to assess change and does not leverage the available soil spatial data, which have helped identify hotspots of ES/ED change and its underlying biophysical character- could have helped identify hotspots of ES/ED change and its underlying biophysical istics (Figure 1). characteristics (Figure 1). Figure 1. The soil “hotspot” concept—an intersection between soil type and land cover classes under Figure 1. The soil “hotspot” concept—an intersection between soil type and land cover classes under natural or anthropogenic dist natural or anthropogenic disturbance urbance (adapt (adapted ed from Bétard and Peulvast from Bétard and Peulvast, , 2019 [14]). 2019 [14]). Previous research on soil C regulating ES in the state of South Carolina estimated Previous research on soil C regulating ES in the state of South Carolina estimated the the avoided social costs of carbon attributed to soil organic carbon (SOC, $107.14B), soil avoided social costs of carbon attributed to soil organic carbon (SOC, $107.14B), soil inor- inorganic carbon (SIC, $17.22B), and total soil carbon (TSC, $124.36B) and was conducted ganic carbon (SIC, $17.22B), and total soil carbon (TSC, $124.36B) and was conducted at at various scales using both biophysical (soil orders) and administrative accounts (state, various scales using both biophysical (soil orders) and administrative accounts (state, re- region, county) [15]. That study used pedodiversity concepts (soil order) integrated with gion, county) [15]. That study used pedodiversity concepts (soil order) integrated with administrative units to report and rank estimates of avoided social costs of soil C, which administrative units to report and rank estimates of avoided social costs of soil C, which provide practical information to design soil C management at various scales [15]. provide practical information to design soil C management at various scales [15]. The present study hypothesizes that pedodiversity concepts overlayed with admin- The present study hypothesizes that pedodiversity concepts overlayed with admin- istrative units (e.g., state) and changes in land cover can be used to identify land cover istrative units (e.g., state) and changes in land cover can be used to identify land cover patterns of soil carbon hotspots for sustainable management (Figure 1). The specific ob- patterns of soil carbon hotspots for sustainable management (Figure 1). The specific ob- jectives of this study are to assess the value of TSC in the state of South Carolina (U.S.A.) jectives of this study are to assess the value of TSC in the state of South Carolina (U.S.A.) by soil order, land cover class, and land cover change based on the social cost of carbon by soil order, land cover class, and land cover change based on the social cost of carbon (SC–CO ) and avoided emissions provided by carbon sequestration, which the U.S. En- (SC–CO2) and avoided emissions provided by carbon sequestration, which the U.S. Envi- vironmental Protection Agency (EPA) has determined to be $46 per metric ton of CO , ronmental Protection Agency (EPA) has determined to be $46 per metric ton of CO2, ap- applicable for the year 2025 based on 2007 U.S. dollars and an average discount rate of plicable for the year 2025 based on 2007 U.S. dollars and an average discount rate of 3% 3% [16]. This study provides monetary values of TSC for soil depth (0–200 cm) across the [16]. This study provides monetary values of TSC for soil depth (0–200 cm) across the state state and different land covers (i.e., barren land, woody wetlands, etc.) using State Soil and different land covers (i.e., barren land, woody wetlands, etc.) using State Soil Geo- Geographic (STATSGO) database, National Land Cover Database (NLCD), and information graphic (STATSGO) database, National Land Cover Database (NLCD), and information previously reported by Guo et al. (2006) [17]. previously reported by Guo et al. (2006) [17]. Earth 2021, 2 676 2. Materials and Methods 2.1. Spatial Analysis Classified land cover data for 2001 and 2016 were downloaded from the Multi- Resolution Land Characteristics Consortium (MRLC) website [18] (Table 1). Changes in land cover were calculated by comparing the 2001 and 2016 land cover maps using the raster calculator function in ArcMap 10.7 [19]. Land cover extent for each of the types of identified land cover (Table 2) was determined by converting the land cover maps to a vector format and then using the union tool with the vector land cover maps and the Soil Survey Geographic database (SSURGO) [20] data layer in ArcMap 10.7 and exporting the results to MS Excel for area analysis (Table 2). The spatial scale of the SSURGO and MRLC is appropriate for associating soil type with land cover change. Table 1. Data sources and descriptions. Data Layer Source Scale/Spatial Resolution (m) Date Google Earth Engine (GEE) data National Land Cover Database (NLCD) provided by the U.S. 30 Geological Survey (USGS) Soil Survey Geographic Database (SSURGO) provided by Soil 10 2016 the National Agricultural Library (NAL) Table 2. The classification system, which is used by National Land Cover Database (NLCD) (adapted from USGS, 2012 [21]). NLCD Land Cover Classes (LULC) Definition Open water All areas of open water, generally with less than 25% cover of vegetation or soil. Thin soil, sand, or rocks, include deserts, dry salt flats, beaches, sand dune, exposed Barren land rock, strip mines, and gravel pits. Forest or shrubland vegetation accounts for >20% of vegetative cover and the soil is Woody wetlands periodically saturated with or covered with water. Natural or semi-natural woody vegetation with aerial stems, generally less than 6 m Shrub/Scrub tall, with individuals or clumps not touching to interlocking. A mixture of broadleaved deciduous and needle-leaved evergreen vegetation, each Mixed forest occupy at least 25% of the area. 75% or more of the tree species shed foliage simultaneously in response to seasonal Deciduous forest change. Plants without persistent stem or shoots above ground and lacking definite firm Herbaceous structure. 75% or more of the tree species maintain their leaves all year. Canopy is never without Evergreen forest green foliage. Emergent herbaceous >75% by herbaceous plants that are hydrophytic or water adapted. wetlands Grasses, legumes, or grass-legume mixtures planted for livestock grazing or the Hay/pasture production of seed or hay crops. Cultivated croplands Areas used to produce crops, such as corn, soybeans, vegetables, tobacco, and cotton. Areas with a mixture of some constructed materials, but mostly vegetation in the form Developed, open space of lawn grasses. Impervious surfaces account for less than 20% of total cover. Areas with a mixture of constructed materials and vegetation. Impervious surfaces Developed, medium account for 50% to 79% of the total cover. These areas most commonly include intensity single-family housing units. Areas with a mixture of constructed materials and vegetation, include single-family Developed, low intensity housing units. Developed, high intensity People reside in high numbers, include apartment complexes and row houses. Earth 2021, 2 677 2.2. The Accounting Framework The present study used both biophysical (science-based) and administrative/land cover (boundary-based) accounts to calculate monetary values for SOC, SIC, and TSC (Tables 3 and 4). Table 3. A conceptual overview of the accounting framework used in this study (adapted from Groshans et al., 2018 [5]). STOCKS FLOWS VALUE Administrative/Land Biophysical Accounts Monetary Account(s) Benefit(s)/Damage(s) Total Value Cover Accounts (Science-Based) (Boundary-Based) Administrative/Land Ecosystem good(s) and Soil extent: Sector: Types of value: cover extent: service(s)/disservices: Composite (total) stock: Total soil carbon (TSC) = Soil organic carbon (SOC) + Soil inorganic carbon (SIC) The social cost of carbon Environment: (SC-CO ) and avoided emissions: - State (South Carolina) - Soil orders (Entisols, - Regions Inceptisols, Histosols, - Regulating (e.g., - $46 per metric ton of CO (2007 - Counties - Sequestered carbon Alfisols, Mollisols, carbon sequestration) U.S. dollars with an average - Land cover classes Spodosols, Ultisols) discount rate of 3% [16]) (LULC) The present study is based on SOC, SIC, and TSC (SOC + SIC) [17] estimates for the SOC, SIC, TSC storage (in Mg or metric tons) and contents (in kg m ) in the contiguous U.S. from Guo et al. (2006) [17]. A monetary valuation for SOC, SIC, TSC was calculated using the social cost of carbon (SC-CO ) of $46 per metric ton of CO , which is applicable 2 2 for 2025 based on 2007 U.S. dollars and an average discount rate of 3% [16]. According to the EPA, the SC-CO is intended to be a comprehensive estimate of climate change damages. Still, it can underestimate the true damages and cost of CO emissions due to the exclusion of various important climate change impacts recognized in the literature [16]. Soil carbon (SC) storage and content estimates were converted to U.S. dollars and dollars per square meter in Microsoft Excel using the following equations, with a social cost of carbon of $46/Mg CO (a metric ton is equivalent to 1 megagram (Mg) or 1000 kilograms (kg)): 44 Mg CO $46 $ = (SC Storage, Mg)   (1) 12 Mg TSC Mg CO $ kg 1 Mg 44 Mg CO $46 = SC Content,    (2) 2 2 3 m m 10 kg 12 Mg TSC Mg CO 2 2 Table 4 presents area-normalized content (kg m ) and monetary values ($ m ) of soil carbon, which were used to estimate TSC storage and TSC value by multiplying corresponding content (values) numbers by an area of a particular soil order within a county, region. For example, for the soil order of Entisols (based on SOC and SIC numbers from Guo et al. (2006) [17]), an area-normalized midpoint TSC content number of 12.8 kgm in the upper 2 m of soil (Table 4) was used to calculate the total TSC storage in soil order by multiplying its area in particular county or region. Then, the reported area-normalized midpoint TSC content number of 12.8 kgm in the upper 2 m (Table 4) was converted to monetary values ($ m ) of TSC using a social cost of carbon (SC-CO ) of $46 per metric ton of CO (2007 U.S. dollars with an average discount rate of 3% [16]), which is $2.17 m and was used to calculate the total monetary value of TSC storage. Earth 2021, 2 678 Earth 2021, 2, FOR PEER REVIEW 5 2 2 Table 4. Area-normalized content (kg m ) and monetary values ($ m ) of soil organic carbon (SOC), soil inorganic carbon (SIC), total soil carbon (TSC) by soil order −2 based on numbers (midpoint −2 soil organic carbon, soil inorganic carbon) in the Table 4. Area-normalized content (kg m ) and monetary values ($ m ) of soil organic carbon (SOC), soil inorganic carbon upper 2-m of the soil from Guo et al., 2006 [17] and a social cost of carbon (SC-CO ) of $46 per metric ton of CO (2007 U.S. (SIC), total soil carbon (TSC) by soil order based on numbers (midpoint soil organic carb 2 on, soil inorganic carbon) in 2 the upper 2-m of dollars with t an he so average il from G discount uo et al., 2006 rate of 3% [17] and a [16]). social cost of carbon (SC-CO2) of $46 per metric ton of CO2 (2007 U.S. dollars with an average discount rate of 3% [16]). Minimum-Midpoint-Maximum Contents Midpoint Values Soil Order Minimum-Midpoint-Maximum Contents Midpoint Values 2 2 2 2 2 2 Soil Order SOC (kg m ) SIC (kg m ) TSC (kg m ) SOC ($ m ) SIC ($ m ) TSC ($ m ) −2 −2 −2 −2 −2 −2 SOC (kg m ) SIC (kg m ) TSC (kg m ) SOC ($ m ) SIC ($ m ) TSC ($ m ) Slightly Weathered Slightly Weathered EntisolsEntisols 1.8 1.8—8.0—15.8 —8.0—15.8 1.9 1.9—4.8—8.4 —4.8—8.4 3 3.7—12.8—24.2 .7—12.8—24.2 1.35 1.35 0.82 0.82 2.17 2.17 Inceptisols 2.8—8.9—17.4 2.5—5.1—8.4 5.3—14.0—25.8 1.50 0.86 2.36 Inceptisols 2.8—8.9—17.4 2.5—5.1—8.4 5.3—14.0—25.8 1.50 0.86 2.36 Histosols 63.9—140.1—243.9 0.6—2.4—5.0 64.5—142.5—248.9 23.62 0.41 24.03 Histosols 63.9—140.1—243.9 0.6—2.4—5.0 64.5—142.5—248.9 23.62 0.41 24.03 Moderately Weathered Moderately Weathered Alfisols 2.3—7.5—14.1 1.3—4.3—8.1 3.6—11.8—22.2 1.27 0.72 1.99 Alfisols 2.3—7.5—14.1 1.3—4.3—8.1 3.6—11.8—22.2 1.27 0.72 1.99 Mollisols 5.9—13.5—22.8 4.9—11.5—19.7 10.8—25.0—42.5 2.28 1.93 4.21 Mollisols 5.9—13.5—22.8 4.9—11.5—19.7 10.8—25.0—42.5 2.28 1.93 4.21 Strongly Weathered Strongly Weathered Spodosols 2.9—12.3—25.5 0.2—0.6—1.1 3.1—12.9—26.6 2.07 0.10 2.17 Spodosols 2.9—12.3—25.5 0.2—0.6—1.1 3.1—12.9—26.6 2.07 0.10 2.17 Ultisols 1.9—7.1—13.9 0.0—0.0—0.0 1.9—7.1—13.9 1.20 0.00 1.20 Ultisols 1.9—7.1—13.9 0.0—0.0—0.0 1.9—7.1—13.9 1.20 0.00 1.20 Note: TSC = SOC + SIC. Note: TSC = SOC + SIC. 3. Results 3. Results 3.1. Land Cover Change in South Carolina 3.1. Land Cover Change in South Carolina Land cover in the state of South Carolina is diverse (Figure 2) with the following Land cover in the state of South Carolina is diverse (Figure 2) with the following distri- distribution including open water (2016, in decreasing order): evergreen forest (23.85%); bution including open water (2016, in decreasing order): evergreen forest (23.85%); woody woody wetlands (19.37%); cultivated croplands (9.05%); deciduous forest (8.84%); wetlands (19.37%); cultivated croplands (9.05%); deciduous forest (8.84%); hay/pasture hay/pasture (6.58%); open water (6.34%); developed, open space (6.12%); mixed forest (6.58%); open water (6.34%); developed, open space (6.12%); mixed forest (5.77%); herba- (5.77%); herbaceous (3.99%); shrub/scrub (3.28%); developed, low intensity (2.70%); emer- ceous (3.99%); shrub/scrub (3.28%); developed, low intensity (2.70%); emergent herbaceous gent herbaceous wetlands (2.61%); developed, medium intensity (0.92%); developed, high wetlands (2.61%); developed, medium intensity (0.92%); developed, high intensity (0.34%); intensity (0.34%); and barren land (0.25%) (Figure 2). Land cover in South Carolina has and barren land (0.25%) (Figure 2). Land cover in South Carolina has undergone changes undergone changes between 2001 and 2016, with a general trend of urbanization leading between 2001 and 2016, with a general trend of urbanization leading to losses in “low to losses in “low disturbance” land covers (e.g., deciduous forest) and gains in developed disturbance” land covers (e.g., deciduous forest) and gains in developed land cover classes land cover classes (e.g., open, low, medium, and high intensity) (Table 5). (e.g., open, low, medium, and high intensity) (Table 5). Figure 2. Land cover map (2016) of South Carolina (SC) (U.S.A.) (33.8361 N, 81.1637 W) (adapted Figure 2. Land cover map (2016) of South Carolina (SC) (U.S.A.) (33.8361° N, 81.1637° W) (adapted from Multi-Resolution Land Characteristics Consortium [18]). from Multi-Resolution Land Characteristics Consortium [18]). Earth 2021, 2 679 Table 5. Land cover change in South Carolina (U.S.A.) between 2001 and 2016 (based on Multi- Resolution Land Characteristics Consortium [18]). 2 2 NLCD Land Cover Classes (LULC) 2001 Area (km ) 2016 Area (km ) Change (%) Barren land 176.71 162.44 8 Woody wetlands 15,944.41 15,769.88 1 Shrub/Scrub 2510.33 2702.75 +8 Mixed forest 4462.97 4717.34 +6 Deciduous forest 7797.41 7231.86 7 Herbaceous 3297.11 3250.81 1 Evergreen forest 19,590.27 19,637.64 0 Emergent herbaceous wetlands 2006.92 2037.10 +2 Hay/Pasture 6014.94 5415.81 10 Cultivated crops 7475.67 7452.51 0 Developed, open space 4671.20 4913.96 +5 Developed, medium intensity 464.39 673.16 +45 Developed, low intensity 1826.46 2080.79 +14 Developed, high intensity 161.60 225.32 +39 3.2. Land Cover Change and Soil Carbon Regulating Ecosystem Services in South Carolina The total estimated mid-point monetary value for TSC in South Carolina in 2016 was $124.4B (i.e., $124.4 billion U.S. dollars, where B = billion = 10 ), with the following mone- tary distribution and percent change in value between 2001 and 2016: barren land ($259.7M, 9%) (i.e., $259.7 million U.S. dollars, where M = million = 10 ); woody wetlands ($33.8B, 1%); shrub/scrub ($3.9B, +9%); mixed forest ($6.9B, +5%); deciduous forest ($10.6B, 7%); herbaceous ($4.8B, 5%); evergreen forest ($28.6B, +1%); emergent herbaceous wetlands ($6.9B, 3%); hay/pasture ($7.3B, 10%); cultivated crops ($9.9B, 0%); developed, open space ($7.0B, +5%); developed, medium intensity ($978M, +46%); developed, low intensity ($2.9B, +15%); and developed, high intensity ($318M, +39%) (Table 6). For a particular land cover, the percent change in monetary value can be different from the percent change in area because different soil orders have different TSC contents. Most soil orders experienced losses in “low disturbance” land covers and gains in developed land cover classes with $1.1B in likely “realized” social cost of carbon. Spodosols experienced dramatic increases (% change) in the developed land covers (Table 7). Table 6. Soil carbon regulating ecosystem services and land cover change in the state of South Carolina (U.S.A.) between 2001 and 2016 based on total soil carbon (TSC) derived from numbers in the upper 2 m of the soil (by soil type) from Guo et al., 2006 [17] and a social cost of carbon (SC-CO ) of $46 per metric ton of CO (2007 U.S. dollars with an average discount 2 2 rate of 3% [16]). NLCD Land Cover Classes (LULC) 2001 Mid. Value ($) 2016 Mid. Value ($) Net Change ($) Change (%) Barren land $284,320,000 $259,710,000 $24,610,000 9 Woody wetlands $34,199,940,000 $33,808,480,000 $391,460,000 1 Shrub/Scrub $3,612,540,000 $3,922,480,000 $309,940,000 +9 Mixed forest $6,587,920,000 $6,946,750,000 $358,830,000 +5 Deciduous forest $11,409,990,000 $10,588,350,000 $821,640,000 7 Herbaceous $5,054,090,000 $4,800,100,000 $253,990,000 5 Evergreen forest $28,470,170,000 $28,641,620,000 $171,450,000 +1 Emergent herbaceous wetlands $7,110,200,000 $6,930,830,000 $179,370,000 3 Hay/Pasture $8,165,800,000 $7,330,030,000 $835,770,000 10 Cultivated crops $9,890,030,000 $9,902,780,000 $12,750,000 0 Developed, open space $6,645,820,000 $7,001,100,000 $355,280,000 +5 Developed, medium intensity $670,520,000 $978,030,000 $307,510,000 +46 Developed, low intensity $2,610,130,000 $2,991,170,000 $381,040,000 +15 Developed, high intensity $228,710,000 $318,150,000 $89,440,000 +39 Note: Mid. = midpoint. Earth 2021, 2 680 Table 7. Land cover change in the state of South Carolina (U.S.A.) between 2001 and 2016 based on soil order. Degree of Weathering and Soil Development Earth 2021, 2, FOR PEER REVIEW 7 Slight Moderate Strong NLCD Land Cover Classes (LULC) Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols Area Change between 2001 and 2016 (%) Table 7. Land cover change in the state of South Carolina (U.S.A.) between 2001 and 2016 based on soil order. Barren land 10.16% 3.10% 15.52% 1.36% 14.12% 10.67% 8.01% Degree of Weathering and Soil Development Woody wetlands 0.48% 1.14% 1.45% 1.59% 0.38% 2.11% 1.24% Slight Moderate Strong Shrub/Scrub 5.56% 32.23% 59.69% 20.51% 53.78% 30.04% 6.57% NLCD Land Cover Classes Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols (LULC) Mixed forest 4.12% 5.29% 12.05% 8.88% 2.88% 1.86% 5.96% Area Change between 2001 and 2016 (%) Deciduous forest 4.45% 6.06% 17.94% 10.67% 0.95% 18.30% 7.11% Barren land −10.16% −3.10% −15.52% −1.36% −14.12% −10.67% −8.01% Herbaceous 5.33% 8.21% 30.16% 27.77% 25.13% 27.32% 4.46% Woody wetlands 0.48% −1.14% −1.45% −1.59% 0.38% −2.11% −1.24% Evergreen forest Shrub/Scrub 5. 1.86% 56% 0.43% 32.23% 7.47%59.69% 20.51% 4.34% −53.78% 6.72% −30.04% 6. 57% 0.71% 0.25% Mixed forest −4.12% 5.29% 12.05% 8.88% 2.88% −1.86% 5.96% Emergent herbaceous wetlands 1.20% 12.41% 7.00% 4.76% 20.64% 3.28% 28.35% Deciduous forest −4.45% −6.06% −17.94% −10.67% 0.95% 18.30% −7.11% Hay/Pasture 11.31% 11.23% 45.01% 12.96% 36.19% 17.19% 9.47% Herbaceous −5.33% −8.21% −30.16% −27.77% −25.13% −27.32% 4.46% Cultivated crops 3.74% 1.33% 5.13% 10.99% 42.90% 0.51% 0.66% Evergreen forest −1.86% 0.43% 7.47% 4.34% 6.72% −0.71% −0.25% Emergent herbaceous wetlands −1.20% 12.41% −7.00% −4.76% −20.64% −3.28% 28.35% Developed, open space 5.50% 5.02% 1.41% 7.43% 9.41% 7.79% 4.88% Hay/Pasture −11.31% −11.23% −45.01% −12.96% −36.19% −17.19% −9.47% Developed, medium intensity 41.72% 38.84% 16.04% 58.07% 65.03% 68.42% 43.36% Cultivated crops 3.74% 1.33% 5.13% 10.99% 42.90% −0.51% −0.66% Developed, low intensity 17.24% 11.30% 3.24% 20.49% 19.75% 31.88% 12.38% Developed, open space 5.50% 5.02% 1.41% 7.43% 9.41% 7.79% 4.88% Developed, high intensity 37.53% 35.67% 16.26% 40.92% 22.54% 78.26% 38.60% Developed, medium intensity 41.72% 38.84% 16.04% 58.07% 65.03% 68.42% 43.36% Developed, low intensity 17.24% 11.30% 3.24% 20.49% 19.75% 31.88% 12.38% Developed, high intensity 37.53% 35.67% 16.26% 40.92% 22.54% 78.26% 38.60% Integration of land cover change analysis with soil carbon regulating ecosystem servicesIallows ntegratioidentification n of land cover cof han “hotspots” ge analysis w of ithpotential soil carbon“r reealized” gulating ec ro egulating system ser-ED due to vices allows identification of “hotspots” of potential “realized” regulating ED due to land land disturbance, which can be described by land cover classes (e.g., developed with disturbance, which can be described by land cover classes (e.g., developed with various various degrees of intensity: open, low, medium, and high). The area of soil type (e.g., degrees of intensity: open, low, medium, and high). The area of soil type (e.g., soil order soil order in this study, Figure 3) can be used to calculate the soil C regulating ES (e.g., the in this study, Figure 3) can be used to calculate the soil C regulating ES (e.g., the social social cost of carbon, SC-CO ) using soil C contents for specific soil orders. There is no cost of carbon, SC-CO2) using soil C contents for specific soil orders. There is no direct direct relationship between the area of the soil order and the social costs of soil C because relationship between the area of the soil order and the social costs of soil C because differ- different soil orders have different soil C contents (Figure 3a,b). ent soil orders have different soil C contents (Figure 3a,b). Figure 3. Soil distribution in the state of South Carolina (U.S.A.) (2016): (a) area of soil orders; (b) value ($) of soil carbon Figure 3. Soil distribution in the state of South Carolina (U.S.A.) (2016): (a) area of soil orders; (b) value ($) of soil carbon regulating ecosystem services based on total soil carbon (TSC) derived from numbers in the upper 2 m of the soil from regulating ecosystem services based on total soil carbon (TSC) derived from numbers in the upper 2 m of the soil from Guo et al., 2006 [17] and a social cost of carbon (SC-CO2) of $46 per metric ton of CO2 (2007 U.S. dollars with an average Guo et al., 2006 [17] and a social cost of carbon (SC-CO ) of $46 per metric ton of CO (2007 U.S. dollars with an average 2 2 discount rate of 3% [16]). discount rate of 3% [16]). Earth 2021, 2 681 Land cover change (between 2001 and 2016) resulted in $1.1B in most likely “realized” SC-CO with the following distribution by soil order: Entisols: $173.6M, 15% from the total; Inceptisols: $54.3M, 5%; Histosols: $0.0, 0%; Alfisols: $151.2M, 13%; Mollisols: $4.2M, 1%; Spodosols: $91.1M, 8%; Ultisols: $658.8M, 58%) (Table 8). Most of the potential “realized” social costs of C were associated with Ultisols, a highly weathered soil covering the largest proportion of the state area (70%). Table 8. Soil carbon regulating ecosystem services and “developed” land cover change in the state of South Carolina (U.S.A.) between 2001 and 2016 based on total soil carbon (TSC) derived from numbers in the upper 2 m of the soil from Guo et al., 2006 [17] and a social cost of carbon (SC-CO ) of $46 per metric ton of CO (2007 U.S. dollars with an average discount rate 2 2 of 3% [16]). Degree of Weathering and Soil Development Slight Moderate Strong NLCD Land Cover Classes (LULC) Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols SC-CO ($) 7 7 7 6 7 8 Developed, open space 4.1  10 2.1  10 0 4.8  10 4.2  10 2.6  10 2.1  10 7 7 7 7 8 Developed, medium intensity 0 0 5.2  10 1.4  10 4.0  10 2.4  10 1.8  10 7 7 7 7 8 Developed, low intensity 6.7  10 1.7  10 0 5.4  10 0 3.5  10 2.1  10 7 6 7 6 7 Developed, high intensity 1.3  10 2.4  10 0 1.0  10 0 6.5  10 5.8  10 9 8 7 8 6 7 8 Totals ($1.1  10 ) 1.7  10 5.4  10 0 1.5  10 4.2  10 9.1  10 6.6  10 3.3. Land Cover Change and Soil Carbon Regulating Ecosystem Services by Soil Order, County, and Region South Carolina experienced changes in land use/land cover (LULC) over the 15-year period from 2001 to 2016, and these changes varied by soil order, county, and region. Most regions and counties experienced area losses in “low disturbance” LULC classes and gains in the “high disturbance” LULC classes (Table 9), which resulted in potential “realized” social costs of carbon from SOC (Tables 10 and 11), SIC (Tables 12 and 13), and TSC (Tables 14 and 15). The Midlands region of the state experienced the highest gains in the “high disturbance” LULC classes and corresponding potential “realized” SC-CO with over $421M for TSC, $354.6M for SOC, and $66.4M for SIC. Among counties, Horry County ranked first with over $142.2M in potential “realized” SC-CO for TSC, followed by Lexington ($103.7M), Richland ($95.3M), Greenville ($81.4M), York ($77.5M), Charleston ($70.7M), Beaufort ($64.1M), Berkeley ($50.9M), Spartanburg ($50.0M), and Aiken ($43.0M) counties. Increases in “high disturbance” LULC classes are commonly associated with already existing urbanized areas in all regions, and several coastal counties. In addition to totals of potential “realized” SC-CO by county, this study also provides a breakdown of SC-CO by soil order for each county. This novel way to combine satellite based LULC change analysis with soil information can provide useful insight on the intersection between LULC change and soil resources. The benefits of this approach can be demonstrated using the results of this study, which shows that most of the potential “realized” SC-CO are associated with the soil order of Ultisols, which are highly leached and erodible soils. Previous research by Werts et al. (2013) [22] has shown that the development process can have additional social costs associated with this land conversion (e.g., erosion, water quality, and human social costs). Spodosols are present in many rapidly developing coastal areas of South Carolina but may not be suitable for development that includes septic systems because of their high saturated conductivity [23]. Several counties had development in agriculturally productive soils (Alfisols and Mollisols) which are commonly considered to be soil carbon hotspots with higher SC-CO . In South Carolina, these agriculturally productive soils are relatively rare and represent less than 10% of the total state area, compared to Ultisols, which cover 70% of South Carolina [15]. Over time, loss of productive agricultural soils to development can reduce local food security. Future planning should consider different soil types to limit the social cost of Earth 2021, 2 682 development. For example, government protections of wetland soils (e.g., Histosols) have limited their development in South Carolina (Table 15). Table 9. Soil diversity (pedodiversity) and “developed” land cover (open space, low, medium, and high intensity) change from 2001 to 2016 by soil order (taxonomic pedodiversity), region, and county in the state of South Carolina (U.S.A.) based on Soil Survey Geographic (SSURGO) Database (2020) [20]. Degree of Weathering and Soil Development Increase in Developed County Area (Region) Slightly Weathered Moderately Weathered Strongly Weathered (km ) Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols (Rank) Increase in Developed Area (km ) between 2001 and 2016 by Soil Order Anderson 30.0 (11) 0.5 0 0 0 0 0 29.5 Cherokee 7.3 (24) 0.6 0.2 0 0.1 0 0 6.4 Greenville 65.5 (2) 1.2 1.5 0 0 0 0 62.7 Oconee 14.4 (16) 0.3 0.2 0 0 0 0 13.9 Pickens 12.4 (17) 0.2 0.1 0 0 0 0 12.1 Spartanburg 39.5 (6) 0.4 1.6 0 0.5 0 0 37 Union 4.2 (28) 0.1 0 0 0.7 0 0 3.4 (Upstate) 173.3 (2) 3.3 3.6 0 1.3 0 0 165 Abbeville 3.2 (32) 0.0 0.0 0 0.6 0 0 2.6 Aiken 30.7 (10) 5.2 0.7 0 0.4 0 0 24.4 Chester 3.7 (30) 0 0.1 0 1.3 0 0 2.3 Edgefield 6.1 (25) 0.7 0.3 0 0.1 0 0 5.0 Fairfield 4.2 (29) 0 0.1 0 0.4 0 0 3.7 Greenwood 8.4 (22) 0 0.2 0 2.3 0 0 5.7 Kershaw 11.9 (19) 3.5 0.2 0 0.1 0 0 8.1 Lancaster 20.8 (12) 0.3 1.0 0 0.4 0 0 19.1 Laurens 8.9 (21) 0.1 0 0 1 0 0 7.8 Lexington 65.4 (3) 22.0 2.6 0 1 0 0.1 39.7 Newberry 4.6 (27) 0 0 0 0.2 0 0 4.4 Richland 63.4 (4) 15.8 2.3 0 1.5 0 0 43.8 Saluda 3.1 (33) 0 0 0 0.2 0 0 2.9 York 51.6 (5) 0.1 0.7 0 18.6 0 0 32.2 (Midlands) 285.8 (1) 47.7 8.2 0 28.1 0 0.1 201.7 Chesterfield 2.9 (34) 0.1 0.6 0 0 0 0 2.2 Clarendon 3.6 (31) 0.1 0.1 0 0 0 0 3.4 Darlington 7.4 (23) 0.4 0.7 0 0 0 0 6.3 Dillon 2.2 (35) 0.3 0 0 0 0 0 1.9 Florence 19.7 (14) 0.2 0.6 0 0 0 0 18.9 Georgetown 9.7 (20) 1.8 1.5 0 2.6 0 1.0 2.8 Horry 84.1 (1) 6.3 3.2 0 10.9 0 23.6 40.1 Lee 1.5 (41) 0.2 0 0 0 0 0 1.3 Marion 1.5 (42) 0 0 0 0 0 0 1.5 Marlboro 1.6 (40) 0 0.1 0 0 0 0 1.5 Sumter 15.4 (15) 0 0.3 0 0 0 0 15.1 Williamsburg 1.9 (38) 0.1 0.1 0 0 0 0 1.7 151.5 (4) 9.5 7.2 0 13.5 0 24.6 96.7 (Pee Dee) Allendale 0.9 (44) 0 0 0 0 0 0 0.9 Bamberg 0.5 (46) 0 0 0 0 0 0 0.5 Barnwell 2.2 (36) 0.3 0 0 0 0 0 1.9 Beaufort 35.8 (9) 9.7 0.8 0 0.9 0.3 9.5 14.6 Berkeley 37.4 (8) 1.3 0.3 0 3.6 0 1.6 30.6 Calhoun 1.8 (39) 0.1 0.1 0 0 0 0 1.6 Charleston 37.5 (7) 5.5 2.1 0 18.8 0 3.2 7.9 Colleton 2.0 (37) 0.2 0.1 0 0 0 0.7 1.0 Dorchester 20.8 (13) 1.3 0 0 7.7 0 0.1 11.7 Hampton 0.6 (45) 0 0 0 0 0 0 0.5 Jasper 12.1 (18) 0.9 0 0 1.8 0.1 0.5 8.8 McCormick 1.3 (43) 0 0 0 0.2 0 0 1.1 Orangeburg 5.8 (26) 0.1 0 0 0.1 0 0 5.6 (Low Country) 158.6 (3) 19.4 3.4 0 33.1 0.4 15.6 86.7 Totals 769.1 79.9 22.4 0 76.0 0.4 40.3 550.1 Earth 2021, 2 683 Table 10. Mid-point soil organic carbon (SOC) storage for newly “developed” land covers (open space, low, medium, and high intensity) from 2001 to 2016 in the state of South Carolina (U.S.A.) based on mid-point SOC content numbers in the upper 2 m of the soil based on data from Guo et al., 2006 [17]. Degree of Weathering and Soil Development County Total SOC Slightly Weathered Moderately Weathered Strongly Weathered (Region) (kg) Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols SOC (kg) 8 6 8 Anderson 2.13  10 4.00  10 0 0 0 0 0 2.09  10 7 6 6 5 7 Cherokee 5.28  10 4.80  10 1.78  10 0 7.50  10 0 0 4.54  10 8 6 7 8 Greenville 0 0 0 0 4.68  10 9.60  10 1.34  10 4.45  10 8 6 6 7 Oconee 0 0 0 0 1.03  10 2.40  10 1.78  10 9.87  10 7 6 5 7 Pickens 8.84  10 1.60  10 8.90  10 0 0 0 0 8.59  10 8 6 7 6 8 Spartanburg 2.84  10 3.20  10 1.42  10 0 3.75  10 0 0 2.63  10 7 5 6 7 Union 3.02  10 8.00  10 0 0 5.25  10 0 0 2.41  10 9 7 7 6 9 (Upstate) 1.24  10 2.64  10 3.20  10 0 9.75  10 0 0 1.17  10 7 6 7 Abbeville 0 0 0 0 0 2.30  10 4.50  10 1.85  10 8 7 6 6 8 Aiken 2.24  10 4.16  10 6.23  10 0 3.00  10 0 0 1.73  10 7 5 6 7 Chester 2.70  10 0 8.90  10 0 9.75  10 0 0 1.63  10 7 6 6 5 7 Edgefield 4.45  10 5.60  10 2.67  10 0 7.50  10 0 0 3.55  10 7 5 6 7 Fairfield 3.02  10 0 8.90  10 0 3.00  10 0 0 2.63  10 7 6 7 7 Greenwood 5.95  10 0 1.78  10 0 1.73  10 0 0 4.05  10 7 7 6 5 7 Kershaw 8.80  10 2.80  10 1.78  10 0 7.50  10 0 0 5.75  10 8 6 6 6 8 Lancaster 0 0 0 1.50  10 2.40  10 8.90  10 3.00  10 1.36  10 7 5 6 7 Laurens 0 0 0 0 6.37  10 8.00  10 7.50  10 5.54  10 8 8 7 6 6 8 Lexington 4.90  10 1.76  10 2.31  10 0 7.50  10 0 1.23  10 2.82  10 7 6 7 Newberry 3.27  10 0 0 0 1.50  10 0 0 3.12  10 8 8 7 7 8 Richland 4.69  10 1.26  10 2.05  10 0 1.13  10 0 0 3.11  10 7 6 7 Saluda 2.21  10 0 0 0 1.50  10 0 0 2.06  10 8 5 6 8 8 York 3.75  10 8.00  10 6.23  10 0 1.40  10 0 0 2.29  10 9 8 7 8 6 9 (Midlands) 2.10  10 3.82  10 7.30  10 0 2.11  10 0 1.23  10 1.43  10 7 5 6 7 Chesterfield 2.18  10 8.00  10 5.34  10 0 0 0 0 1.56  10 7 5 5 7 Clarendon 2.58  10 8.00  10 8.90  10 0 0 0 0 2.41  10 7 6 6 7 Darlington 5.42  10 3.20  10 6.23  10 0 0 0 0 4.47  10 7 6 7 Dillon 1.59  10 2.40  10 0 0 0 0 0 1.35  10 8 6 6 8 Florence 1.41  10 1.60  10 5.34  10 0 0 0 0 1.34  10 7 7 7 7 7 7 Georgetown 7.94  10 1.44  10 1.34  10 0 1.95  10 0 1.23  10 1.99  10 8 7 7 7 8 8 Horry 0 0 7.36  10 5.04  10 2.85  10 8.18  10 2.90  10 2.85  10 7 6 6 Lee 1.08  10 1.60  10 0 0 0 0 0 9.23  10 7 7 Marion 1.07  10 0 0 0 0 0 0 1.07  10 7 5 7 Marlboro 1.15  10 0 8.90  10 0 0 0 0 1.07  10 8 6 8 Sumter 1.10  10 0 2.67  10 0 0 0 0 1.07  10 7 5 5 7 Williamsburg 1.38  10 8.00  10 8.90  10 0 0 0 0 1.21  10 9 7 7 8 8 8 (Pee Dee) 1.23  10 7.60  10 6.41  10 0 1.01  10 0 3.03  10 6.87  10 6 6 Allendale 6.39  10 0 0 0 0 0 0 6.39  10 6 6 Bamberg 3.55  10 0 0 0 0 0 0 3.55  10 7 6 7 Barnwell 1.59  10 2.40  10 0 0 0 0 0 1.35  10 8 7 6 6 6 8 8 Beaufort 3.16  10 7.76  10 7.12  10 0 6.75  10 4.05  10 1.17  10 1.04  10 8 7 6 7 7 8 Berkeley 2.77  10 1.04  10 2.67  10 0 2.70  10 0 1.97  10 2.17  10 7 5 5 7 Calhoun 1.31  10 8.00  10 8.90  10 0 0 0 0 1.14  10 8 7 7 8 7 7 Charleston 0 0 2.99  10 4.40  10 1.87  10 1.41  10 3.94  10 5.61  10 7 6 5 6 6 Colleton 1.82  10 1.60  10 8.90  10 0 0 0 8.61  10 7.10  10 8 7 7 6 7 Dorchester 1.52  10 1.04  10 0 0 5.78  10 0 1.23  10 8.31  10 6 6 Hampton 3.55  10 0 0 0 0 0 0 3.55  10 7 6 7 6 6 7 Jasper 9.07  10 7.20  10 0 0 1.35  10 1.35  10 6.15  10 6.25  10 6 6 6 McCormick 9.31  10 0 0 0 1.50  10 0 0 7.81  10 7 5 5 7 Orangeburg 4.13  10 8.00  10 0 0 7.50  10 0 0 3.98  10 9 8 7 8 6 8 8 (Low Country) 1.25  10 1.55  10 3.03  10 0 2.48  10 5.40  10 1.92  10 6.16  10 9 8 8 8 6 8 9 Totals 5.82  10 6.39  10 1.99  10 0 5.70  10 5.40  10 4.96  10 3.91  10 Earth 2021, 2 684 Table 11. The total dollar value of mid-point soil organic carbon (SOC) storage value for newly “developed” land covers (open space, low, medium, and high intensity) from 2001 to 2016 in the state of South Carolina (U.S.A.) based on a social cost of carbon (SC-CO ) of $46 per metric ton of CO (2007 U.S. dollars with an average discount rate of 3% [16]). 2 2 Degree of Weathering and Soil Development Total County SC-CO 2 Slightly Weathered Moderately Weathered Strongly Weathered (Region) ($) Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols SC-CO ($) 7 5 7 Anderson 3.61  10 6.75  10 0 0 0 0 0 3.54  10 6 5 5 5 6 Cherokee 8.92  10 8.10  10 3.00  10 0 1.27  10 0 0 7.68  10 7 6 6 7 Greenville 0 0 0 0 7.91  10 1.62  10 2.25  10 7.52  10 7 5 5 7 Oconee 0 0 0 0 1.74  10 4.05  10 3.00  10 1.67  10 7 5 5 7 Pickens 1.49  10 2.70  10 1.50  10 0 0 0 0 1.45  10 7 5 6 5 7 Spartanburg 4.80  10 5.40  10 2.40  10 0 6.35  10 0 0 4.44  10 6 5 5 6 Union 5.10  10 1.35  10 0 0 8.89  10 0 0 4.08  10 8 6 6 6 8 (Upstate) 2.10  10 4.46  10 5.40  10 0 1.65  10 0 0 1.98  10 6 5 6 Abbeville 0 0 0 0 0 3.88  10 7.62  10 3.12  10 7 6 6 5 7 Aiken 3.79  10 7.02  10 1.05  10 0 5.08  10 0 0 2.93  10 6 5 6 6 Chester 4.56  10 0 1.50  10 0 1.65  10 0 0 2.76  10 6 5 5 5 6 Edgefield 7.52  10 9.45  10 4.50  10 0 1.27  10 0 0 6.00  10 6 5 5 6 Fairfield 5.10  10 0 1.50  10 0 5.08  10 0 0 4.44  10 7 5 6 6 Greenwood 1.01  10 0 3.00  10 0 2.92  10 0 0 6.84  10 7 6 5 5 6 Kershaw 1.49  10 4.73  10 3.00  10 0 1.27  10 0 0 9.72  10 7 5 6 5 7 Lancaster 0 0 0 2.53  10 4.05  10 1.50  10 5.08  10 2.29  10 7 5 6 6 Laurens 0 0 0 0 1.08  10 1.35  10 1.27  10 9.36  10 7 7 6 6 5 7 Lexington 8.27  10 2.97  10 3.90  10 0 1.27  10 0 2.07  10 4.76  10 6 5 6 Newberry 5.53  10 0 0 0 2.54  10 0 0 5.28  10 7 7 6 6 7 Richland 7.92  10 2.13  10 3.45  10 0 1.91  10 0 0 5.26  10 6 5 6 Saluda 3.73  10 0 0 0 2.54  10 0 0 3.48  10 7 5 6 7 7 York 6.34  10 1.35  10 1.05  10 0 2.36  10 0 0 3.86  10 8 7 7 7 5 8 (Midlands) 3.55  10 6.44  10 1.23  10 0 3.57  10 0 2.07  10 2.42  10 6 5 5 6 Chesterfield 3.68  10 1.35  10 9.00  10 0 0 0 0 2.64  10 6 5 5 6 Clarendon 4.37  10 1.35  10 1.50  10 0 0 0 0 4.08  10 6 5 6 6 Darlington 9.15  10 5.40  10 1.05  10 0 0 0 0 7.56  10 6 5 6 Dillon 2.69  10 4.05  10 0 0 0 0 0 2.28  10 7 5 5 7 Florence 2.39  10 2.70  10 9.00  10 0 0 0 0 2.27  10 7 6 6 6 6 6 Georgetown 1.34  10 2.43  10 2.25  10 0 3.30  10 0 2.07  10 3.36  10 8 6 6 7 7 7 Horry 0 0 1.24  10 8.51  10 4.80  10 1.38  10 4.89  10 4.81  10 6 5 6 Lee 1.83  10 2.70  10 0 0 0 0 0 1.56  10 6 6 Marion 1.80  10 0 0 0 0 0 0 1.80  10 6 5 6 Marlboro 1.95  10 0 1.50  10 0 0 0 0 1.80  10 7 5 7 Sumter 1.86  10 0 4.50  10 0 0 0 0 1.81  10 6 5 5 6 Williamsburg 2.33  10 1.35  10 1.50  10 0 0 0 0 2.04  10 8 7 7 7 7 8 (Pee Dee) 2.08  10 1.28  10 1.08  10 0 1.71  10 0 5.09  10 1.16  10 6 6 Allendale 1.08  10 0 0 0 0 0 0 1.08  10 5 5 Bamberg 6.00  10 0 0 0 0 0 0 6.00  10 6 5 6 Barnwell 2.69  10 4.05  10 0 0 0 0 0 2.28  10 7 7 6 6 5 7 7 Beaufort 5.33  10 1.31  10 1.20  10 0 1.14  10 6.84  10 1.97  10 1.75  10 7 6 5 6 6 7 Berkeley 4.68  10 1.76  10 4.50  10 0 4.57  10 0 3.31  10 3.67  10 6 5 5 6 Calhoun 2.21  10 1.35  10 1.50  10 0 0 0 0 1.92  10 7 6 6 7 6 6 Charleston 0 0 5.06  10 7.43  10 3.15  10 2.39  10 6.62  10 9.48  10 6 5 5 6 6 Colleton 3.07  10 2.70  10 1.50  10 0 0 0 1.45  10 1.20  10 7 6 6 5 7 Dorchester 2.58  10 1.76  10 0 0 9.78  10 0 2.07  10 1.40  10 5 5 Hampton 6.00  10 0 0 0 0 0 0 6.00  10 7 6 6 5 6 7 Jasper 1.53  10 1.22  10 0 0 2.29  10 2.28  10 1.04  10 1.06  10 6 5 6 McCormick 1.57  10 0 0 0 2.54  10 0 0 1.32  10 6 5 5 6 Orangeburg 6.98  10 1.35  10 0 0 1.27  10 0 0 6.72  10 8 7 6 7 5 7 8 (Low Country) 2.11  10 2.62  10 5.10  10 0 4.20  10 9.12  10 3.23  10 1.04  10 8 8 7 7 5 7 8 Totals 9.82  10 1.08  10 3.36  10 0 9.65  10 9.12  10 8.34  10 6.60  10 Earth 2021, 2 685 Table 12. Mid-point soil inorganic carbon (SIC) storage for newly “developed” land covers (open space, low, medium, and high intensity) from 2001 to 2016 in the state of South Carolina (U.S.A.) based on mid-point SIC content numbers in the upper 2 m of the soil based on data from Guo et al., 2006 [17]. Degree of Weathering and Soil Development County Total SIC Slightly Weathered Moderately Weathered Strongly Weathered (Region) (kg) Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols SIC (kg) 6 6 Anderson 2.40  10 2.40  10 0 0 0 0 0 0 6 6 6 5 Cherokee 4.33  10 2.88  10 1.02  10 0 4.30  10 0 0 0 7 6 6 Greenville 0 0 0 0 0 1.34  10 5.76  10 7.65  10 6 6 6 Oconee 0 0 0 0 0 2.46  10 1.44  10 1.02  10 6 5 5 Pickens 1.47  10 9.60  10 5.10  10 0 0 0 0 0 7 6 6 6 Spartanburg 1.22  10 1.92  10 8.16  10 0 2.15  10 0 0 0 6 5 6 Union 3.49  10 4.80  10 0 0 3.01  10 0 0 0 7 7 7 6 (Upstate) 3.98  10 1.58  10 1.84  10 0 5.59  10 0 0 0 6 6 Abbeville 0 0 0 0 0 0 2.58  10 2.58  10 7 7 6 6 Aiken 3.03  10 2.50  10 3.57  10 0 1.72  10 0 0 0 6 5 6 Chester 6.10  10 0 5.10  10 0 5.59  10 0 0 0 6 6 6 5 Edgefield 5.32  10 3.36  10 1.53  10 0 4.30  10 0 0 0 6 5 6 Fairfield 2.23  10 0 5.10  10 0 1.72  10 0 0 0 7 6 6 Greenwood 1.09  10 0 1.02  10 0 9.89  10 0 0 0 7 7 6 5 Kershaw 1.83  10 1.68  10 1.02  10 0 4.30  10 0 0 0 6 6 6 6 Lancaster 0 0 0 0 8.26  10 1.44  10 5.10  10 1.72  10 6 5 6 Laurens 0 0 0 0 0 4.78  10 4.80  10 4.30  10 8 8 7 6 4 Lexington 1.23  10 1.06  10 1.33  10 0 4.30  10 0 6.00  10 0 5 5 Newberry 8.60  10 0 0 0 8.60  10 0 0 0 7 7 7 6 Richland 9.40  10 7.58  10 1.17  10 0 6.45  10 0 0 0 5 5 Saluda 8.60  10 0 0 0 8.60  10 0 0 0 7 5 6 7 York 8.40  10 4.80  10 3.57  10 0 8.00  10 0 0 0 8 8 7 8 4 (Midlands) 3.92  10 2.29  10 4.18  10 0 1.21  10 0 6.00  10 0 6 5 6 Chesterfield 3.54  10 4.80  10 3.06  10 0 0 0 0 0 5 5 5 Clarendon 9.90  10 4.80  10 5.10  10 0 0 0 0 0 6 6 6 Darlington 5.49  10 1.92  10 3.57  10 0 0 0 0 0 6 6 Dillon 1.44  10 1.44  10 0 0 0 0 0 0 6 5 6 Florence 4.02  10 9.60  10 3.06  10 0 0 0 0 0 7 6 6 7 5 Georgetown 2.81  10 8.64  10 7.65  10 0 1.12  10 0 6.00  10 0 8 7 7 7 7 Horry 0 0 0 1.08  10 3.02  10 1.63  10 4.69  10 1.42  10 5 5 Lee 9.60  10 9.60  10 0 0 0 0 0 0 Marion 0 0 0 0 0 0 0 0 5 5 Marlboro 5.10  10 0 5.10  10 0 0 0 0 0 6 6 Sumter 0 0 0 0 0 0 1.53  10 1.53  10 5 5 5 Williamsburg 0 0 0 0 0 9.90  10 4.80  10 5.10  10 8 7 7 7 7 (Pee Dee) 1.55  10 4.56  10 3.67  10 0 5.81  10 0 1.48  10 0 Allendale 0 0 0 0 0 0 0 0 Bamberg 0 0 0 0 0 0 0 0 6 6 Barnwell 1.44  10 1.44  10 0 0 0 0 0 0 7 7 6 6 6 6 Beaufort 6.37  10 4.66  10 4.08  10 0 3.87  10 3.45  10 5.70  10 0 7 6 6 7 5 Berkeley 0 0 0 2.42  10 6.24  10 1.53  10 1.55  10 9.60  10 5 5 5 Calhoun 0 0 0 0 0 9.90  10 4.80  10 5.10  10 8 7 7 7 6 Charleston 1.20  10 2.64  10 1.07  10 0 8.08  10 0 1.92  10 0 6 5 5 5 Colleton 1.89  10 9.60  10 5.10  10 0 0 0 4.20  10 0 7 6 7 4 Dorchester 3.94  10 6.24  10 0 0 3.31  10 0 6.00  10 0 Hampton 0 0 0 0 0 0 0 0 7 6 6 6 5 Jasper 1.35  10 4.32  10 0 0 7.74  10 1.15  10 3.00  10 0 5 5 McCormick 8.60  10 0 0 0 8.60  10 0 0 0 5 5 5 Orangeburg 9.10  10 4.80  10 0 0 4.30  10 0 0 0 8 7 7 8 6 6 (Low Country) 2.67  10 9.31  10 1.73  10 0 1.42  10 4.60  10 9.36  10 0 8 8 8 8 6 7 Totals 8.53  10 3.84  10 1.14  10 0 3.27  10 4.60  10 2.42  10 0 Earth 2021, 2 686 Table 13. The total dollar value of mid-point soil inorganic carbon (SIC) storage value for newly “developed” land covers (open space, low, medium, and high intensity) from 2001 to 2016 in the state of South Carolina (U.S.A.) based on a social cost of carbon (SC-CO ) of $46 per metric ton of CO (2007 U.S. dollars with an average discount rate of 3% [16]). 2 2 Degree of Weathering and Soil Development County Total (Region) SC-CO 2 Slightly Weathered Moderately Weathered Strongly Weathered ($) Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols SC-CO ($) 5 5 Anderson 4.10  10 4.10  10 0 0 0 0 0 0 5 5 5 4 Cherokee 7.36  10 4.92  10 1.72  10 0 7.20  10 0 0 0 6 5 6 Greenville 0 0 0 0 0 2.27  10 9.84  10 1.29  10 5 5 5 Oconee 0 0 0 0 0 4.18  10 2.46  10 1.72  10 5 5 4 Pickens 2.50  10 1.64  10 8.60  10 0 0 0 0 0 6 5 6 5 Spartanburg 2.06  10 3.28  10 1.38  10 0 3.60  10 0 0 0 5 4 5 Union 5.86  10 8.20  10 0 0 5.04  10 0 0 0 6 6 6 5 (Upstate) 6.74  10 2.71  10 3.10  10 0 9.36  10 0 0 0 5 5 Abbeville 0 0 0 0 0 0 4.32  10 4.32  10 6 6 5 5 Aiken 5.15  10 4.26  10 6.02  10 0 2.88  10 0 0 0 6 4 5 Chester 1.02  10 0 8.60  10 0 9.36  10 0 0 0 5 5 5 4 Edgefield 9.04  10 5.74  10 2.58  10 0 7.20  10 0 0 0 5 4 5 Fairfield 3.74  10 0 8.60  10 0 2.88  10 0 0 0 6 5 6 Greenwood 1.83  10 0 1.72  10 0 1.66  10 0 0 0 6 6 5 4 Kershaw 3.11  10 2.87  10 1.72  10 0 7.20  10 0 0 0 6 5 5 5 Lancaster 0 0 0 0 1.39  10 2.46  10 8.60  10 2.88  10 5 4 5 Laurens 0 0 0 0 0 8.02  10 8.20  10 7.20  10 7 7 6 5 4 Lexington 2.10  10 1.80  10 2.24  10 0 7.20  10 0 1.00  10 0 5 5 Newberry 1.44  10 0 0 0 1.44  10 0 0 0 7 7 6 6 Richland 1.60  10 1.30  10 1.98  10 0 1.08  10 0 0 0 5 5 Saluda 1.44  10 0 0 0 1.44  10 0 0 0 7 4 5 7 York 1.41  10 8.20  10 6.02  10 0 1.34  10 0 0 0 7 7 6 7 4 (Midlands) 6.64  10 3.91  10 7.05  10 0 2.02  10 0 1.00  10 0 5 4 5 Chesterfield 5.98  10 8.20  10 5.16  10 0 0 0 0 0 5 4 4 Clarendon 1.68  10 8.20  10 8.60  10 0 0 0 0 0 5 5 5 Darlington 9.30  10 3.28  10 6.02  10 0 0 0 0 0 5 5 Dillon 2.46  10 2.46  10 0 0 0 0 0 0 5 5 5 Florence 6.80  10 1.64  10 5.16  10 0 0 0 0 0 6 6 6 6 5 Georgetown 4.74  10 1.48  10 1.29  10 0 1.87  10 0 1.00  10 0 7 6 6 6 6 Horry 0 0 0 1.81  10 5.17  10 2.75  10 7.85  10 2.36  10 5 5 Lee 1.64  10 1.64  10 0 0 0 0 0 0 Marion 0 0 0 0 0 0 0 0 4 4 Marlboro 8.60  10 0 8.60  10 0 0 0 0 0 5 5 Sumter 0 0 0 0 0 0 2.58  10 2.58  10 5 4 4 Williamsburg 0 0 0 0 0 1.68  10 8.20  10 8.60  10 7 6 6 6 6 (Pee Dee) 2.62  10 7.79  10 6.19  10 0 9.72  10 0 2.46  10 0 Allendale 0 0 0 0 0 0 0 0 Bamberg 0 0 0 0 0 0 0 0 5 5 Barnwell 2.46  10 2.46  10 0 0 0 0 0 0 7 6 5 5 5 5 Beaufort 1.08  10 7.95  10 6.88  10 0 6.48  10 5.79  10 9.50  10 0 6 6 5 6 5 Berkeley 0 0 0 4.08  10 1.07  10 2.58  10 2.59  10 1.60  10 5 4 4 Calhoun 0 0 0 0 0 1.68  10 8.20  10 8.60  10 7 6 6 7 5 Charleston 2.02  10 4.51  10 1.81  10 0 1.35  10 0 3.20  10 0 5 5 4 4 Colleton 3.20  10 1.64  10 8.60  10 0 0 0 7.00  10 0 6 6 6 4 Dorchester 6.62  10 1.07  10 0 0 5.54  10 0 1.00  10 0 Hampton 0 0 0 0 0 0 0 0 6 5 6 5 4 Jasper 2.28  10 7.38  10 0 0 1.30  10 1.93  10 5.00  10 0 5 5 McCormick 1.44  10 0 0 0 1.44  10 0 0 0 5 4 4 Orangeburg 1.54  10 8.20  10 0 0 7.20  10 0 0 0 7 7 6 7 5 6 (Low Country) 4.50  10 1.59  10 2.92  10 0 2.38  10 7.72  10 1.56  10 0 8 7 7 7 5 6 Totals 1.44  10 6.55  10 1.93  10 0 5.47  10 7.72  10 4.03  10 0 Earth 2021, 2 687 Table 14. Mid-point total soil carbon (TSC) storage for newly “developed” land covers (open space, low, medium, and high intensity) from 2001 to 2016 in the state of South Carolina (U.S.A.) based on mid-point SIC content numbers in the upper 2 m of the soil based on data from Guo et al., 2006 [17]. Degree of Weathering and Soil Development County Total TSC Slightly Weathered Moderately Weathered Strongly Weathered (Region) (kg) Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols TSC (kg) 8 6 8 Anderson 2.16  10 6.40  10 0 0 0 0 0 2.09  10 7 6 6 6 7 Cherokee 5.71  10 7.68  10 2.80  10 0 1.18  10 0 0 4.54  10 8 7 7 8 Greenville 0 0 0 0 4.82  10 1.54  10 2.10  10 4.45  10 8 6 6 7 Oconee 0 0 0 0 1.05  10 3.84  10 2.80  10 9.87  10 7 6 6 7 Pickens 8.99  10 2.56  10 1.40  10 0 0 0 0 8.59  10 8 6 7 6 8 Spartanburg 2.96  10 5.12  10 2.24  10 0 5.90  10 0 0 2.63  10 7 6 6 7 Union 3.37  10 1.28  10 0 0 8.26  10 0 0 2.41  10 9 7 7 7 9 (Upstate) 1.28  10 4.22  10 5.04  10 0 1.53  10 0 0 1.17  10 7 6 7 Abbeville 0 0 0 0 0 2.55  10 7.08  10 1.85  10 8 7 6 6 8 Aiken 2.54  10 6.66  10 9.80  10 0 4.72  10 0 0 1.73  10 7 6 7 7 Chester 3.31  10 0 1.40  10 0 1.53  10 0 0 1.63  10 7 6 6 6 7 Edgefield 4.98  10 8.96  10 4.20  10 0 1.18  10 0 0 3.55  10 7 6 6 7 Fairfield 3.24  10 0 1.40  10 0 4.72  10 0 0 2.63  10 7 6 7 7 Greenwood 7.04  10 0 2.80  10 0 2.71  10 0 0 4.05  10 8 7 6 6 7 Kershaw 1.06  10 4.48  10 2.80  10 0 1.18  10 0 0 5.75  10 8 6 7 6 8 Lancaster 0 0 0 1.58  10 3.84  10 1.40  10 4.72  10 1.36  10 7 6 7 7 Laurens 0 0 0 0 6.85  10 1.28  10 1.18  10 5.54  10 8 8 7 7 6 8 Lexington 6.13  10 2.82  10 3.64  10 0 1.18  10 0 1.29  10 2.82  10 7 6 7 Newberry 3.36  10 0 0 0 2.36  10 0 0 3.12  10 8 8 7 7 8 Richland 5.63  10 2.02  10 3.22  10 0 1.77  10 0 0 3.11  10 7 6 7 Saluda 2.30  10 0 0 0 2.36  10 0 0 2.06  10 8 6 6 8 8 York 4.59  10 1.28  10 9.80  10 0 2.19  10 0 0 2.29  10 9 8 8 8 6 9 (Midlands) 2.49  10 6.11  10 1.15  10 0 3.32  10 0 1.29  10 1.43  10 7 6 6 7 Chesterfield 2.53  10 1.28  10 8.40  10 0 0 0 0 1.56  10 7 6 6 7 Clarendon 2.68  10 1.28  10 1.40  10 0 0 0 0 2.41  10 7 6 6 7 Darlington 5.97  10 5.12  10 9.80  10 0 0 0 0 4.47  10 7 6 7 Dillon 1.73  10 3.84  10 0 0 0 0 0 1.35  10 8 6 6 8 Florence 1.45  10 2.56  10 8.40  10 0 0 0 0 1.34  10 8 7 7 7 7 7 Georgetown 1.08  10 2.30  10 2.10  10 0 3.07  10 0 1.29  10 1.99  10 8 7 7 8 8 8 Horry 0 0 8.43  10 8.06  10 4.48  10 1.29  10 3.04  10 2.85  10 7 6 6 Lee 1.18  10 2.56  10 0 0 0 0 0 9.23  10 7 7 Marion 1.07  10 0 0 0 0 0 0 1.07  10 7 6 7 Marlboro 1.21  10 0 1.40  10 0 0 0 0 1.07  10 8 6 8 Sumter 1.11  10 0 4.20  10 0 0 0 0 1.07  10 7 6 6 7 Williamsburg 1.48  10 1.28  10 1.40  10 0 0 0 0 1.21  10 9 8 8 8 8 8 (Pee Dee) 1.39  10 1.22  10 1.01  10 0 1.59  10 0 3.17  10 6.87  10 6 6 Allendale 6.39  10 0 0 0 0 0 0 6.39  10 6 6 Bamberg 3.55  10 0 0 0 0 0 0 3.55  10 7 6 7 Barnwell 1.73  10 3.84  10 0 0 0 0 0 1.35  10 8 8 7 7 6 8 8 Beaufort 3.80  10 1.24  10 1.12  10 0 1.06  10 7.50  10 1.23  10 1.04  10 8 7 6 7 7 8 Berkeley 3.01  10 1.66  10 4.20  10 0 4.25  10 0 2.06  10 2.17  10 7 6 6 7 Calhoun 1.40  10 1.28  10 1.40  10 0 0 0 0 1.14  10 8 7 7 8 7 7 Charleston 0 0 4.19  10 7.04  10 2.94  10 2.22  10 4.13  10 5.61  10 7 6 6 6 6 Colleton 0 0 0 2.01  10 2.56  10 1.40  10 9.03  10 7.10  10 8 7 7 6 7 Dorchester 1.92  10 1.66  10 0 0 9.09  10 0 1.29  10 8.31  10 6 6 Hampton 3.55  10 0 0 0 0 0 0 3.55  10 8 7 7 6 6 7 Jasper 1.04  10 1.15  10 0 0 2.12  10 2.50  10 6.45  10 6.25  10 7 6 6 McCormick 1.02  10 0 0 0 2.36  10 0 0 7.81  10 7 6 6 7 Orangeburg 4.22  10 1.28  10 0 0 1.18  10 0 0 3.98  10 9 8 7 8 7 8 8 (Low Country) 1.51  10 2.48  10 4.76  10 0 3.91  10 1.00  10 2.01  10 6.16  10 9 9 8 8 7 8 9 Totals 6.67  10 1.02  10 3.14  10 0 8.97  10 1.00  10 5.20  10 3.91  10 Earth 2021, 2 688 Table 15. The total dollar value of mid-point total soil carbon (TSC) storage value for newly “developed” land covers (open space, low, medium, and high intensity) from 2001 to 2016 in the state of South Carolina (U.S.A.) based on a social cost of carbon (SC-CO ) of $46 per metric ton of CO (2007 U.S. dollars with an average discount rate of 3% [16]). 2 2 Degree of Weathering and Soil Development Total County SC-CO 2 Slightly Weathered Moderately Weathered Strongly Weathered (Region) ($) Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols SC-CO ($) 7 6 7 Anderson 3.65  10 1.09  10 0 0 0 0 0 3.54  10 6 6 5 5 6 Cherokee 9.65  10 1.30  10 4.72  10 0 1.99  10 0 0 7.68  10 7 6 6 7 Greenville 0 0 0 0 8.14  10 2.60  10 3.54  10 7.52  10 7 5 5 7 Oconee 0 0 0 0 1.78  10 6.51  10 4.72  10 1.67  10 7 5 5 7 Pickens 1.52  10 4.34  10 2.36  10 0 0 0 0 1.45  10 7 5 6 5 7 Spartanburg 5.00  10 8.68  10 3.78  10 0 9.95  10 0 0 4.44  10 6 5 6 6 Union 5.69  10 2.17  10 0 0 1.39  10 0 0 4.08  10 8 6 6 6 8 (Upstate) 2.16  10 7.16  10 8.50  10 0 2.59  10 0 0 1.98  10 6 6 6 Abbeville 0 0 0 0 0 4.31  10 1.19  10 3.12  10 7 7 6 5 7 Aiken 4.30  10 1.13  10 1.65  10 0 7.96  10 0 0 2.93  10 6 5 6 6 Chester 5.58  10 0 2.36  10 0 2.59  10 0 0 2.76  10 6 6 5 5 6 Edgefield 8.43  10 1.52  10 7.08  10 0 1.99  10 0 0 6.00  10 6 5 5 6 Fairfield 5.47  10 0 2.36  10 0 7.96  10 0 0 4.44  10 7 5 6 6 Greenwood 1.19  10 0 4.72  10 0 4.58  10 0 0 6.84  10 7 6 5 5 6 Kershaw 1.80  10 7.60  10 4.72  10 0 1.99  10 0 0 9.72  10 7 5 6 5 7 Lancaster 0 0 0 2.67  10 6.51  10 2.36  10 7.96  10 2.29  10 7 5 6 6 Laurens 0 0 0 0 1.16  10 2.17  10 1.99  10 9.36  10 8 7 6 6 5 7 Lexington 1.04  10 4.77  10 6.14  10 0 1.99  10 0 2.17  10 4.76  10 6 5 6 Newberry 5.68  10 0 0 0 3.98  10 0 0 5.28  10 7 7 6 6 7 Richland 9.53  10 3.43  10 5.43  10 0 2.99  10 0 0 5.26  10 6 5 6 Saluda 3.88  10 0 0 0 3.98  10 0 0 3.48  10 7 5 6 7 7 York 7.75  10 2.17  10 1.65  10 0 3.70  10 0 0 3.86  10 8 8 7 7 5 8 (Midlands) 4.21  10 1.04  10 1.94  10 0 5.59  10 0 2.17  10 2.42  10 6 5 6 6 Chesterfield 4.27  10 2.17  10 1.42  10 0 0 0 0 2.64  10 6 5 5 6 Clarendon 4.53  10 2.17  10 2.36  10 0 0 0 0 4.08  10 7 5 6 6 Darlington 1.01  10 8.68  10 1.65  10 0 0 0 0 7.56  10 6 5 6 Dillon 2.93  10 6.51  10 0 0 0 0 0 2.28  10 7 5 6 7 Florence 2.45  10 4.34  10 1.42  10 0 0 0 0 2.27  10 7 6 6 6 6 6 Georgetown 1.82  10 3.91  10 3.54  10 0 5.17  10 0 2.17  10 3.36  10 8 7 6 7 7 7 Horry 0 0 1.42  10 1.37  10 7.55  10 2.17  10 5.12  10 4.81  10 6 5 6 Lee 1.99  10 4.34  10 0 0 0 0 0 1.56  10 6 6 Marion 1.80  10 0 0 0 0 0 0 1.80  10 6 5 6 Marlboro 2.04  10 0 2.36  10 0 0 0 0 1.80  10 7 5 7 Sumter 1.88  10 0 7.08  10 0 0 0 0 1.81  10 6 5 5 6 Williamsburg 2.49  10 2.17  10 2.36  10 0 0 0 0 2.04  10 8 7 7 7 7 8 (Pee Dee) 2.34  10 2.06  10 1.70  10 0 2.69  10 0 5.34  10 1.16  10 6 6 Allendale 1.08  10 0 0 0 0 0 0 1.08  10 5 5 Bamberg 6.00  10 0 0 0 0 0 0 6.00  10 6 5 6 Barnwell 2.93  10 6.51  10 0 0 0 0 0 2.28  10 7 7 6 6 6 7 7 Beaufort 6.41  10 2.10  10 1.89  10 0 1.79  10 1.26  10 2.06  10 1.75  10 7 6 5 6 6 7 Berkeley 5.09  10 2.82  10 7.08  10 0 7.16  10 0 3.47  10 3.67  10 6 5 5 6 Calhoun 2.37  10 2.17  10 2.36  10 0 0 0 0 1.92  10 7 7 6 7 6 6 Charleston 0 0 7.07  10 1.19  10 4.96  10 3.74  10 6.94  10 9.48  10 6 5 5 6 6 Colleton 3.39  10 4.34  10 2.36  10 0 0 0 1.52  10 1.20  10 7 6 7 5 7 Dorchester 3.24  10 2.82  10 0 0 1.53  10 0 2.17  10 1.40  10 5 5 Hampton 6.00  10 0 0 0 0 0 0 6.00  10 7 6 6 5 6 7 Jasper 1.76  10 1.95  10 0 0 3.58  10 4.21  10 1.09  10 1.06  10 6 5 6 McCormick 1.72  10 0 0 0 3.98  10 0 0 1.32  10 6 5 5 6 Orangeburg 7.14  10 2.17  10 0 0 1.99  10 0 0 6.72  10 8 7 6 7 6 7 8 (Low Country) 2.56  10 4.21  10 8.02  10 0 6.59  10 1.68  10 3.39  10 1.04  10 9 8 7 8 6 7 8 Totals 1.13  10 1.73  10 5.29  10 0 1.51  10 1.68  10 8.75  10 6.60  10 Earth 2021, 2 689 4. Discussion 4.1. Determining “Hotspots” of Social Costs of C from Land Cover Change and Pedodiversity Nationally, patterns of LULC are changing rapidly due to urbanization and agricul- tural expansion [9], with regional [24], state [25], and county [12] differences. Increasingly LULC change analysis is conducted in conjunction with the ES framework [12], with lim- ited applications of soil ES/ED. The Southeastern United States and South Carolina are experiencing some of the highest urbanization rates and losses of ecosystem services [12,22]. Mikhailova et al. (2020) [15] examined soil C regulating ES in the state of South Carolina and reported the value of the social cost of carbon dioxide (SC-CO ) emissions by soil C type (SOC, SIC, TSC), soil order, region, and county in the context of “avoided” versus potential “realized” social costs of carbon. Although informative, that study lacked inte- gration with LULC change analysis to indicate “hotspots” of potential “realized” social costs of C because of disturbance (change in LULC), especially in the “developed” LULC category. The present study integrates soil information with LULC change, which provides detailed information about SC-CO by LULC and soil type, as well as the LULC change by soil type. It is especially important because different soil types have different spatial distributions within the state and even association with a particular LULC (e.g., Histosols are commonly found in woody wetlands; emergent herbaceous wetlands), which can make them particularly sensitive to LULC changes and corresponding social costs. This study demonstrates that most of the soils have experienced losses in “low disturbance” LULC (e.g., woody wetlands, herbaceous) and gains in open, low, medium, and high intensity developed land cover classes with $1.1B in most likely “realized” social cost of C primarily associated with Ultisols ($658.8M). Ultisols are highly leached and highly erodible soils that occupy 70% of the area of South Carolina. It should be noted that both the state of South Carolina and Ultisols in particular, historically, experienced tremendous social costs associated with agricultural production in the form of high soil erosion [26], carbon loss, depletion of soil nutrients, and even “socioecological collapse that only state-initiated intervention” could address [27]. There is a legacy of various social costs associated with land and soil degradation in the state of South Carolina [27], which are only now being examined within the framework of ES/ED [12,15]. This legacy demonstrates a common trend with other places in the world, which involves maximizing provisioning ES at the expense of regulating ES and resulting in high social costs at the expense of environment and ordinary people [28,29]. The advantage of the present study is that it uses satellite-based remote sensing to accurately identify land cover and the areas with changing land cover [30], which is important because the land cover and land cover change are likely directly related to the level of disturbance [31]. Land cover maps derived from satellites allow the spatially explicit tracking of land cover and land cover change [32,33]. Many studies that examine the relationship of land cover change to ES rely on fixed estimates for soil carbon that are assumed for each land cover category, instead of relying on estimates from existing soil databases [12]. This study combines the spatial information from land cover maps with soil carbon estimates from soil databases to identify hotspots of disturbance (Figure 4). Because land cover maps are regularly updated [32], it is possible to use this framework to identify and monitor likely SC-CO hotspots over time and calculate cumulative costs associated with land cover change. Future improvements to the study methodology would be to include actual field samples, which would be more accurate than soil order averages; however, the soil carbon content trends are likely the same between the different soil types [11]. Linking this type of study to land cover change models would help inform decision-makers about potential impacts caused by land development [28]. With the existing database of satellite images for at least the last 49 years [34], it will be possible to track land cover change and disturbance over time to estimate the impact of development and understand the future trajectory of disturbance. Earth 2021, 2, FOR PEER REVIEW 17 Earth 2021, 2 690 Figure 4. The total dollar value of mid-point total soil carbon (TSC) storage value for newly “devel- Figure 4. The total dollar value of mid-point total soil carbon (TSC) storage value for newly “de- veloped” oped” land land covers covers (open space, (open space, low low, medium, , medium, and and high intensity) from high intensity) from 2001 2001 to 2016 to 2016 in in the the state state of of South Carolina (U.S.A.) based on a social cost of carbon (SC-CO2) of $46 per metric ton of CO2 South Carolina (U.S.A.) based on a social cost of carbon (SC-CO ) of $46 per metric ton of CO (2007 2 2 (2007 U.S. dollars with an average discount rate of 3% [17]). U.S. dollars with an average discount rate of 3% [16]). 4.2. Determining Potential for C Sequestration Using LULC and Pedodiversity 4.2. Determining Potential for C Sequestration using LULC and Pedodiversity Recarbonization potential is dependent on both LULC and pedodiversity, which Recarbonization potential is dependent on both LULC and pedodiversity, which should be considered together for sustainable carbon management at various administra- should be considered together for sustainable carbon management at various administra- tive levels (e.g., state, region, county, etc.) (Table 16). For example, Table 16 demonstrates tive levels (e.g., state, region, county, etc.) (Table 16). For example, Table 16 demonstrates LULC by soil order at the state level, which shows the inherent soil capacity (based on soil LULC by soil order at the state level, which shows the inherent soil capacity (based on soil properties) for C sequestration and LULC covers (either natural, managed, or in combina- properties) for C sequestration and LULC covers (either natural, managed, or in combi- tion). According to Table 16, most LULC in the state of South Carolina are dominated by nation). According to Table 16, most LULC in the state of South Carolina are dominated the soil order of Ultisols, which have a low recarbonization potential because these soils by the soil order of Ultisols, which have a low recarbonization potential because these are highly weathered with low nutrient holding capacity [35]. Cherkinsky et al. (2018) [36] soils are highly weathered with low nutrient holding capacity [35]. Cherkinsky et al. reported that land use has remarkable effects on Ultisols and forest root systems at great (2018) [36] reported that land use has remarkable effects on Ultisols and forest root sys- depth, which can result in “sluggish regeneration rates of forest root systems” compared to tems at great depth, which can result in “sluggish regeneration rates of forest root sys- aboveground regeneration. tems” compared to aboveground regeneration. Land covers and their changes are often associated with “human-dimension is- Land covers and their changes are often associated with “human-dimension issues” sues” [37], which have a direct impact on soil C. Clay et al. (2019) [38] reported that [37], which have a direct impact on soil C. Clay et al. (2019) [38] reported that there is a there is a significant potential for South Carolina forests to sequester C in the aboveground significant potential for South Carolina forests to sequester C in the aboveground biomass, biomass, but soil C sequestration potential would be limited based on other studies for soils but soil C sequestration potential would be limited based on other studies for soils in the in the state [35,36]. Because most of the South Carolina forestland is in private ownership, state [35,36]. Because most of the South Carolina forestland is in private ownership, forest forest landowners play an important role in decision making with regards to potential landowners play an important role in decision making with regards to potential forest forest management practices aimed at C sequestration both in aboveground biomass and management practices aimed at C sequestration both in aboveground biomass and in the in the soil [39]. According to Richter et al. (1999) [40], soil C losses because of deforestation soil [39]. According to Richter et al. (1999) [40], soil C losses because of deforestation and and cultivation are well-documented, but studies on “soil-carbon recovery after cultivation cultivation are well-documented, but studies on “soil-carbon recovery after cultivation are limited”. Richter et al. (1999) [40] used data from a four-decade-long field study of are limited.” Richter et al. (1999) [40] used data from a four-decade-long field study of C C accumulation by pine ecosystems grown on previously cultivated soils at the Calhoun accumulation by pine ecosystems grown on previously cultivated soils at the Calhoun Experimental Forest in South Carolina (USA) and concluded that “despite high C inputs to Experimental Forest in South Carolina (USA) and concluded that “despite high C inputs the mineral soil, C sequestration was limited by rapid decomposition, facilitated by the to the mineral soil, C sequestration was limited by rapid decomposition, facilitated by the coarse soil texture and low-activity clay mineralogy”. coarse soil texture and low-activity clay mineralogy.” Earth 2021, 2 691 Table 16. Land use/land cover (LULC) by soil order in the state of South Carolina (USA) in 2016. Degree of Weathering and Soil Development 2016 Total Slight Moderate Strong NLCD Land Cover Classes Area by LULC Entisols Inceptisols Histosols Alfisols Mollisols Spodosols Ultisols (LULC) (km ) (%) 2016 Area by Soil Order (% from Total Area in Each LULC) Barren land 162.4 (0.2%) 28.6 4.2 0.1 7.0 0.1 2.2 57.9 Woody wetlands 15,769.9 (20.7%) 9.4 25.1 1.9 8.5 1.1 2.7 51.3 Shrub/Scrub 2702.8 (3.5%) 10.1 4.0 0.1 8.7 0.0 1.2 75.8 Mixed forest 4717.3 (6.2%) 5.2 8.3 0.0 14.1 0.1 0.3 72.0 Deciduous forest 7231.9 (9.5%) 6.3 8.5 0.0 11.6 0.1 0.2 73.4 Herbaceous 3250.8 (4.3%) 12.0 4.5 0.2 8.0 0.0 1.0 74.3 Evergreen forest 19,637.6 (25.7%) 5.9 4.3 0.1 12.5 0.1 2.4 74.6 Emergent herbaceous wetlands 2037.1 (2.7%) 74.1 4.7 6.1 2.2 0.4 0.9 11.6 Hay/Pasture 5415.8 (7.1%) 4.4 3.3 0.0 8.0 0.0 0.5 83.8 Cultivated crops 7452.5 (9.8%) 3.6 3.3 0.2 1.1 0.0 1.0 90.8 Developed, open space 4914.0 (6.4%) 7.4 4.0 0.1 7.0 0.1 3.1 78.3 Developed, medium intensity 673.2 (0.9%) 12.3 2.8 0.0 8.0 0.0 4.0 73.0 Developed, low intensity 2080.8 (2.7%) 10.1 3.2 0.0 7.6 0.0 3.1 75.9 Developed, high intensity 225.3 (0.3%) 10.3 2.4 0.0 8.0 0.0 2.7 76.6 4.3. Significance of Results for South Carolina At present, state of South Carolina has no comprehensive state climate strategy [41]. However, in 2008 a special task force appointed by the South Carolina governor recom- mended more than 50 ways to stop rising greenhouse gas pollution from worsening global warming [42], which included policy recommendations for various economic sectors (e.g., tourism and recreation; agriculture and forestry, etc.). Our study here contributes impor- tant scientific findings and information that supports the final report of the special task force [42] in the following ways: Inventory of South Carolina’s Greenhouse Gas Emissions. Current SC GHG inven- tories do not include soil and LULC analysis. This study quantified and valued the soil C regulating ES for SOC, SIC, and TSC for the state of South Carolina by soil type and LULC. According to the final report by the South Carolina Climate, Energy, and Commerce Committee [42]: “South Carolina’s gross emissions of GHGs grew by 39% between 1990 and 2005, twice the national average of 16%. South Carolina’s emissions growth was driven by the growth of its population and many other factors. In addition, the state’s emissions on a per-capita basis increased by about 15% between 1990 and 2005, while U.S. per-capita emissions declined slightly (2%) over this period due to many other factors. South Carolina’s gross GHG emissions are projected to rise fairly steeply to about 125 MMtCO2e by 2025, or 87% over 1990 levels”. Areas that developed in South Carolina over a 15-year time period were evaluated by the combination of soil type, LULC, and county to calculate the potential realized SC-CO from this land conversion. By identifying these potential SC-CO hotspots, future land conversions could be reduced to help lower future greenhouse gas emissions. Agriculture, Forestry and Waste Management (AFW): AFW-6a: Terrestrial Carbon Sequestration—Agriculture. This study provides spa- tial information about soil C stocks by soil type, LULC (including agricultural LULC), and associated SC-CO over time, which can be used to create a database to monitor the effectiveness of C sequestration using a cost-benefit analysis. AFW-6bi: Terrestrial Carbon Sequestration—Forestry: Forest Management. This study provides spatial information about soil C stocks by soil type, LULC (including different forest LULC), and associated SC-CO over time, which can be used to create a database to monitor the effectiveness of C sequestration using a cost-benefit analysis. AFW-6bii: Terrestrial Carbon Sequestration—Forestry: Afforestation/Reforestation. This study provides spatial information about soil C stocks by soil type, LULC (including different forest LULC) and associated SC-CO over time, which can be used to create a database to monitor the effectiveness of C sequestration using a cost-benefit analysis. Earth 2021, 2 692 AFW-6biii: Terrestrial Carbon Sequestration—Forestry: Urban Forestry. This study provides spatial information about soil C stocks by soil type, LULC (including different forest, developed LULC), and associated SC-CO over time, which can be used to create a database to monitor the effectiveness of C sequestration using a cost-benefit analysis. AFW-7a: Conservation and Restoration of Agriculture Lands for Enhanced Carbon Sequestration. This study provides information about soil C stocks by soil type, LULC (including agricultural LULC), and associated SC-CO over time, which can be used to create a database to monitor the effectiveness of C sequestration using a cost-benefit analysis. AFW-7b: Conservation and Restoration of Forestlands for Enhanced Carbon Se- questration. This study provides information about soil C stocks by soil type, LULC (including different forest LULC), and associated SC-CO over time, which can be used to create a database to monitor the effectiveness of C sequestration using a cost-benefit analysis. Transportation and Land Use (TLU): TLU-4: Improve Development Patterns. This study quantifies SC-CO as “avoided” and “realized” cost, which can be used for cost-benefit analysis when evaluating the development plans. Cross-Cutting (CC) Issues: CC-1: Inventories and Forecasting. This study provides spatial information about soil C stocks by soil type, LULC, and associated SC-CO over time, which can be used in already existing inventories and forecasting efforts. CC-2: GHG Reporting and Registry. This study reported spatial information about soil C stocks by soil type, LULC, and associated SC-CO over time, which can be used in GHG reporting and registry. CC-3: State Government GHG Emissions. This study reported spatial information about soil C stocks by soil type, LULC, and associated SC-CO over time at the state level. CC-4: Comprehensive Local Government Climate Actions Plans (Counties, Cities, etc.). This study reported spatial information about soil C stocks by soil type, LULC, and associated SC-CO overtime at the county level. CC-5: Public Education and Outreach. Results from this study can be used for vari- ous educational (e.g., soil science courses, textbooks, etc.) and extension efforts (e.g., infor- mal education not only with the agricultural communities but also with developers, etc.). CC-6: Adaptation and Vulnerability. Spatial information about the SC-CO hotspots can be used to optimize economic opportunity while minimizing environmental impact. 5. Conclusions This study examined the integration of soil diversity (pedodiversity) concepts (tax- onomic) with land cover change analysis to value soil C regulating ES/ED in the state of South Carolina (U.S.A.) to be considered in territorial planning. Both pedodiversity and land cover information provide critical contexts (e.g., “portfolio-effect”, “distribution- effect,” “evenness-effect,” etc.) for analyzing, interpreting, and reporting ES/ED within the ES framework for sustainable management of soil carbon within the state. Soil carbon is either protected in the soil or subject to release to the atmosphere because of disturbance which depends on the land cover and land cover change. The percent change (between 2001 and 2016) both in areas and monetary values varied by soil order and land cover, with most soil orders experiencing losses in “low disturbance” land covers (e.g., woody wetlands, herbaceous) and gains in open, low, medium, and high intensity developed land cover classes with $1.1B in most likely “realized” social cost of carbon with the following distribution by soil order: Entisols: $173.6M, 15% from the total; Inceptisols: $54.3M, 5%; Histosols: $0.0, 0%; Alfisols: $151.2M, 13%; Mollisols: $4.2M, 1%; Spodosols: $91.1M, 8%; Ultisols: $658.8M, 58%). Most of the “realized” social costs of C were associated with Ultisols, a highly weathered soil covering the largest proportion of the state (70%). It is Earth 2021, 2 693 important to consider the social cost of soil C associated with land cover categories as well as their change in value over time (between 2001 and 2016) when determining if the soil carbon is likely stable or at risk. Considering the land covers with the largest social cost of soil C based on area and soil type, such as woody wetlands and evergreen forests, had very little change, 1% and +1% respectively. The total social cost of soil C for deciduous forest was reduced by 7%; similarly, hay/pasture was reduced by 10%, and barren land was reduced by 9%. Cultivated crops remained unchanged, while herbaceous land cover was reduced by 5%, and emergent herbaceous wetlands were reduced by 3%. Mixed forests increased by 5%, and shrub/scrub land increased by 9%. The developed categories increased between 2001 and 2016. Social cost soil C of developed open space increased by 5%, while developed low intensity increased by 15%. The social cost of soil C for developed medium intensity increased by 46% and developed high intensity increased by 39%. The percent change in monetary values was different from the percent change in areas because different soil orders have different TSC contents, which are dependent on the degree of soil weathering and development, with highly weathered soils having lower TSC compared to moderately weathered soils. The Midlands region experienced the highest gains in the “high disturbance” classes and corresponding SC-CO with over $421M for TSC, $354.6M for SOC, and $66.4M for SIC. Among counties, Horry County ranked first with over $142.2M in SC-CO for TSC, followed by Lexington ($103.7M), Rich- land ($95.3M), Greenville ($81.4M), York ($77.5M), Charleston ($70.7M), Beaufort ($64.1M), Berkeley ($50.9M), Spartanburg ($50.0M), and Aiken ($43.0M) counties. Administrative area of the state combined with pedodiversity and land cover change concepts can provide useful information to design cost-efficient policies to manage soil carbon regulating ES at the state level. Results of this study were linked with the proposed policy recommendations by South Carolina’s Climate, Energy, and Commerce Advisory Committee (CECAC). Author Contributions: Conceptualization, E.A.M.; methodology, E.A.M., M.A.S., and L.L.; formal analysis, E.A.M.; writ-ing—original draft preparation, E.A.M.; writing—review and editing, E.A.M., C.J.P., G.C.P., and M.A.S.; visualization, L.L., H.A.Z., and Z.H. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: We would like to thank the reviewers for their constructive comments and suggestions. Conflicts of Interest: The authors declare no conflict of interest. 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Journal

Earth – Multidisciplinary Digital Publishing Institute

Published: Sep 26, 2021

Keywords: accounting; carbon emissions; CO2; inorganic; organic; regulating; soil; urbanization

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