Spatiotemporal Analysis of Influencing Factors of Carbon Emission in Public Buildings in China
Spatiotemporal Analysis of Influencing Factors of Carbon Emission in Public Buildings in China
Du, Zhuoqun;Liu, Yisheng;Zhang, Zhidong
2022-03-31 00:00:00
buildings Article Spatiotemporal Analysis of Influencing Factors of Carbon Emission in Public Buildings in China Zhuoqun Du * , Yisheng Liu and Zhidong Zhang School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China; yshliu1@bjtu.edu.cn (Y.L.); davidzhang7@163.com (Z.Z.) * Correspondence: duzhu0qun@163.com Abstract: The rapid development of public buildings has greatly increased the country’s energy consumption and carbon emissions. Excessive carbon emissions contribute to global warming. This paper aims to measure the carbon emissions in the operation of public buildings, and to identify the multiple influencing factors of carbon emissions in operational public buildings. First, the spatial and temporal variation characteristics of carbon emissions from public buildings in 30 provinces of China from 2008–2019 are analyzed. Second, a green building index is constructed, and the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model is utilized to explore the relationship between each influencing factor and carbon emissions, using spatial and temporal geographically weighted regression analysis. The results show that the effects of population, urbanization rate, GDP per capita, green building index, and industrial structure on carbon emissions from public buildings all show spatial correlation and differences. There are east-west differences in the operational carbon emissions of public buildings in China’s provinces. Cluster evolution shows a spatially increasing trend from west to east. To some extent, policymakers can develop appropriate policies for different provinces through the findings. Keywords: GTWR; public buildings’ carbon emission; spatiotemporal analysis Citation: Du, Z.; Liu, Y.; Zhang, Z. Spatiotemporal Analysis of Influencing Factors of Carbon Emission in Public Buildings in China. Buildings 2022, 12, 424. 1. Introduction https://doi.org/10.3390/buildings Climate change has become one of the most important globally recognized issues. Reducing CO emissions helps mitigate climate change, and carbon emission and environ- Academic Editors: Shi-Jie Cao and mental protection issues have aroused the attention of various countries. Many countries Wei Feng have begun to measure carbon emissions and take action to reduce them [1,2]. Recently, the Chinese government announced that it strives to achieve carbon peaking by 2030, and Received: 22 February 2022 carbon neutrality by 2060. More detailed plans have also been specified to reach this goal. Accepted: 28 March 2022 By 2030, carbon dioxide emissions per unit of GDP will drop 65% compared to 2005, the Published: 31 March 2022 proportion of non-fossil energy consumption will reach about 25%, and the total installed Publisher’s Note: MDPI stays neutral capacity of solar and wind power generation will reach more than 1.2 billion kilowatts. with regard to jurisdictional claims in The Ministry of Ecology and Environment has proposed that during the 14th and 15th published maps and institutional affil- Five-Year Plan periods, China will carry out CO emission peaking actions and specify the iations. peaking targets and action plans for localities and industries. China’s total construction carbon emissions were 4.93 billion tons in 2018, accounting for 51% of the national carbon emissions. Carbon emissions from the production phase of building materials account for 28% of the total national carbon emissions, the construction phase accounts for 1%, and the Copyright: © 2022 by the authors. building operation phase is 22% [3]. Licensee MDPI, Basel, Switzerland. According to the data in 2018, the existing stock of public buildings in China’s urban This article is an open access article and rural areas is 12.8 billion square meters, accounting for 21.3% of the total civil construc- distributed under the terms and tion area. From the annual data, the energy consumption for the construction of public conditions of the Creative Commons buildings accounted for 44% of the total building construction energy consumption in 2018, Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ and the total energy consumption for the operation of public buildings excluding northern 4.0/). heating accounted for 33% of the total building operation energy consumption [4]. Buildings 2022, 12, 424. https://doi.org/10.3390/buildings12040424 https://www.mdpi.com/journal/buildings Buildings 2022, 12, 424 2 of 16 In 2015, the energy consumption of public buildings in China was 34.1 billion tons of standard coal equivalent, accounting for about 40% of the total energy consumption of civil buildings. However, the public building area accounted for only 18% of the total area of civil buildings. Furthermore, a study showed that the energy intensity of public buildings in China was four times that of residential buildings from 2000 to 2015. Public buildings are important places for human activities, and their construction, operation, renewal, and demolition processes all generate significant energy consumption. Therefore, the CO emissions of public buildings in China have attracted more attention [5]. Green buildings play a very important role in reducing carbon emissions. Wu et al. (2017) [6] found that with commercial buildings in China, green buildings are lower in carbon emissions than non-green buildings, in the operational phase. The Chinese government proposed the Green Building Creation Initiative in 2020, which aims to reach 70% of the green building area in new urban buildings in that year by 2022. The effect of green building on carbon reduction in the building sector and its spatial evolution is the focus of this study. 2. Literature Review 2.1. Spatiotemporal Analysis of Carbon Emission Spatiotemporal analysis is a method that considers both temporal data and spatial position, and is mainly used to solve how coherent entities change over time. Scholars have performed many researches with different methods to study the spatiotemporal analysis of carbon emission and its influencing factors in different fields. For example, Chen et al. (2021) analyzed the temporal and spatial characteristics of industrial carbon emissions in four regions of Guangdong province from 2005 to 2015, and concluded that industrial carbon emissions have a trend of eastward expansion [7]. The spatial dynamic analysis model (SDDM) was used to study the impact of different technological progress factors on carbon emissions [8]. Cui et al. (2021) explored spatiotemporal dynamic evolution of carbon emission intensity and per capita carbon emissions from planting industry in 31 provinces in China across 20 years. The spatial inequality is measured by Theil index and its contribu- tion rate [9]. Wang et al. (2020) employed the standard deviation ellipse method and tapio decoupling method to reveal the spatiotemporal characteristics of the relationship between carbon emissions from transportation industry and economic growth [10]. Hu et al. (2020) studied the spatial and temporal evolution relationship between economic growth and carbon emissions in Belt and Road countries [11]. Han et al. (2021) revealed the spatiotem- poral characteristics of carbon intensity of 20 industries by extending the spatial weight matrix and spatial dubin model [12]. Falahatkar et al. (2020) quantified the relationship between carbon dioxide emission and urban form in 15 Iranian cities, and believed that carbon dioxide emission level was positively correlated with urban area growth and urban complexity increase [13]. Some scholars have studied the carbon emission spatiotemporal effect in the construction industry. Bai et al. (2021) estimated the building inventory and carbon emissions embodied by buildings in 31 provinces of China from 1997 to 2016, and proposed a spatiotemporal decomposition model to identify driving forces [14]. To sum up, in the construction industry, there are few spatial analyses on carbon emissions during the operation of public buildings. 2.2. Influencing Factors of Buildings Carbon Emission The extraction of raw materials, on-site construction activities, and building operations produced the majority of carbon emissions of the construction sector [4]. Hard coal and its derivatives were the largest carbon dioxide emitters in China’s construction industry [15]. In view of the significant impact of the construction industry, prior studies have been carried out to investigate influencing factors in order to develop mitigation strategies. For example, Lu et al. (2016) have analyzed the influencing factors of carbon emissions from construction activities in China, including energy intensity, energy structure, unit cost, level of construction automation, and machine efficiency [16]. Similarly, Zhang, Yan et al. (2019) Buildings 2022, 12, 424 3 of 16 stated that building scale, building structure type, and production efficiency of material are the three main driving factors [4]. Wu, shen et al. (2019) used the STIRPAT model and found that the impact of population size, per capita GDP, energy intensity, and indus- trial structure on carbon emissions were heterogeneous among regions [17]. Mostafavi, Tahsildoost et al. concluded that strengthening the design parameters of envelope structure, optimizing the layout, and utilizing natural ventilation are conducive to reducing energy consumption of high-rises [18]. Tan, Lai, Gu, Zeng, & Li constructed a carbon emission prediction model including population, urbanization rate, and urban building area [19]. Wang et al. explored the driving forces of energy-related CO emissions in the construction industry by implementing the comprehensive decomposition method, and finally found that technological progress of industrial output was the leading factor that suppressed CO emissions [20]. Huang et al. propounded increased energy efficiency design for new buildings and energy-saving retrofit for existing buildings to carbon emission [21]. 2.3. Research Gap Based on a critical review of relevant studies, as well as substantive surveys and interviews with Chinese building industry professionals, we conclude that the spatial and temporal effects of carbon emissions from public buildings still require further research. Because using the STIRPAT model to decompose influence factor is more comprehensive, it is still worthwhile to use this model to study the factors that drive the carbon emission of operational public buildings. 3. Data Source and Methodologies 3.1. Study Area China has 34 provincial districts. Four provincial districts (Tibet, Macao, Hong Kong, and Taiwan) are excluded due to data unavailability, so this study selected a total of 30 provincial districts. The study divided 30 provincial districts into four regions (East region, Central region, West region, and Northeast region) according to the National Bureau of Statistics (Table 1). Table 1. Four regions and their provincial districts. Regions Provincial District East region Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan Central region Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan West region Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang Northeast region Liaoning, Jilin, Heilongjiang 3.2. Data Source The data of green buildings from 2008 to 2016 and 2018 to 2019 are obtained from the Chinese Green Building Evaluation Label Network. Green building data in 2017 are obtained through compilation of public information of green building projects on the website of provincial Construction Department. The remaining indicators are from China Energy Statistical Yearbook and China Statistical Yearbook for 2009–2020. The map data comes from the National Geomatics Center of China. 3.3. Methodology 3.3.1. Hot Spot Analysis G statistic is used to analyze the spatial aggregation degree of carbon emissions during the operation of provincial public buildings, as shown in Equation (1). n n G (d) = w (d)x / x (1) å i j i å i i=1 i=1 Buildings 2022, 12, 424 4 of 16 x is the attribute value of the unit i, and x is the carbon emission in ith province i i public buildings during its operation; w is the spatial weight matrix. i j G is standardized by Equation (2) and the result is Z(G ). The larger the value Z(G ) i i i is, the higher the spatial clustering in the region, indicating that it belongs to the hot spot area; the smaller it is, the lower the spatial clustering in the region, indicating it belongs to the cold spot area. G E(G ) i i Z(G ) = q (2) Var(G ) where E(G ), Var(G ) are the expectation and variance of G , respectively. i i i 3.3.2. Geographically and Temporally Weighted Regression Model The geographically and temporally weighted regression (GTWR) model is a deepening of the geographically weighted regression model, as shown in Equation (3). By using regional panel data for spatial regression, the temporal attributes are linked to the spatial attributes in the GTWR model, which better reflects the spatial and temporal change information of the study area, and makes the estimation results more effective. y = b (u , v , t ) + b (u , v , t )x + # (3) i i i i å k i i i ik i k=1 where y is the dependent variable of sample i, x is the kth independent variable at the ik sample point i, u , v are the latitude and longitude coordinates of the center of grav- i i ity, respectively, (u , v , t ) are the spatial and temporal coordinates of the ith sample, i i i b (u , v , t ) is the regression coefficient on the kth independent variable at the ith sam- k i i i ple point, b (u , v , t ) is the space-time intercept of the ith sample point, and # is the i i i i residual term. 3.3.3. Model Specification Enrlich and Holdren first put forward the classic IPAT model in the early 1970s, which stipulates the influence of external factors on the environment. External factors include population size (P), affluency (A), and technology (T). The IPAT model was improved and transformed into the nonlinear random STIRPAT model, which is often used to analyze influencing factors of carbon emissions in different industries [22]. For example, Ma et al. surveyed the driving factors of carbon dioxide emission from public buildings in a coun- try [23,24]; Yang and Jia explored the spatial effects of technology progress channels on CO emissions for the agricultural, industrial, construction, transportation, and wholesale sectors [25]. The STIRPAT model is expressed as Equation (4): b c d I = aP A T # (4) i i i i i where i denotes the regional unit. I , P , A and T represent the impacts on the environment i i i i owing to population, affluence (per capita GDP), and technology factors in region i, respec- tively. Constant a represents the scale of the model. Meanwhile, b, c, and d are the estimated coefficients of population, affluence (per capita GDP), and technology, respectively. # is the random error term. We take the logarithm of the STIRPAT model, obtaining the following Equation (5): ln I = ln a + b ln P + c ln A + d ln T + ln # (5) i i i i The STIRPAT equation allows the addition of plenty of relevant variables, and the transformation of the model into an extended version, as long as the dimensionality of these variables is reasonable [26,27]. In order to deeply explore the mechanism of carbon emission of public buildings, considering green buildings’ specific characteristics, and looking for supporting references from a great deal of relevant previous studies, this study developed an extended version Buildings 2022, 12, 424 5 of 16 of the STIRPAT model using several meaningful variables retrieved from the population, affluence, and technology levels, respectively. The extended STIRPAT model is expressed in Equation (6): ln C = ln a + b (u , v , t ) + b (u , v , t ) ln P + b (u , v , t ) ln U + b (u , v , t ) ln I S it 0 i i i 1 i i i it 2 i i i it 3 i i i it (6) +b (u , v , t ) ln G + b (u , v , t ) ln I GB + ln # 4 i i i it 5 i i i it i where C refers to the carbon emission in the public building sector in province i over time it t. (u , v , t ) represents spatial coordinates of province i (i = 1, 2, 3, ..., 30). b (k = 1, 2, 3, 4, i i i k 5) denotes the kth regression coefficient in the ith province. The meaning and units of the variables are shown in Table 2. Table 2. Declaration of the model variables. Nomenclature Variable Unit Type Supporting References Carbon emission of public C tCO dependent variable buildings P Population Ten thousand people explained variable [28] U Urbanization level % explained variable [24,29,30] IS Industrial structure 1 explained variable [24,30,31] G GDP per capita Yuan explained variable [32] IGB Index of green buildings explained variable 3.3.4. Index Calculation 1. Calculation methods of CO emission The operational energy consumption of public buildings includes heating, air con- ditioning, ventilation, lighting, elevators, cooking, domestic hot water, office electrical equipment, and comprehensive service equipment and facilities. Corresponding energy types include electricity, gas (natural gas, gas, and LPG), fuel oil (diesel), and coal combus- tion. This study uses a macro model for measuring carbon emissions from buildings based on energy balance sheets. This paper mainly studies the operational stage of carbon emissions in public buildings. Because China’s energy statistics yearbook does not provide building energy consumption directly [33], we need to select the energy consumption as public buildings’ operational consumption. The specific accounting boundaries are shown in Table 3. Table 3. Specific accounting boundary of public building. Chinese Region Terminal Energy Category in Statistics Yearbook Chinese Region Terminal Energy Category in Statistics Yearbook Wholesale, retail trade and hotel, restaurants, and others Coal, electricity, heat, liquefied petroleum gas, natural gas This study mainly measures carbon emissions during the use of public buildings. Carbon emission in Chinese public buildings is measured by the end-use consumption of energy in each region in the China Energy Statistics Yearbook. The industries involved in public buildings are Transport, Storage and Post, Wholesale and Retail Trades, Hotels and Catering Services, and Other. Energy type measurement includes coal, electricity, natural gas, LPG, and thermal power. Oil is not counted because it is mostly used in public buildings for transportation involving cars, and is not counted as energy consumption inside buildings for the time being. To obtain more meaningful and comprehensive results, we included three types of energy sources, such as coal, natural gas, and liquefied petroleum gas. According to the calculation method provided by IPCC (Equation (7)), coal, electricity, and heat consumed in the operation of public buildings are taken as the sources of carbon emissions. Buildings 2022, 12, 424 6 of 16 30 3 30 3 C = ( C + C + C ) = ( E O LCV CF + E de + E d ) (7) å å i j ej hj å å i j i j i j i j ej hj h j i j i where C denotes the total carbon emissions from public building operation in each province, C refers to carbon emission of the consumption of fossil energy i in the j province, and i j C and C represent the carbon emissions from the secondary energy consumption of ej hj electricity and heat in the j province. E denotes the consumption of fossil energy i in the i j j province; O refers to the oxidation rate of the fossil energy i in the province j; LCV i j i j represents the average low-level calorific value of fossil energy i in province j; CF denotes i j the carbon emission factor of fossil energy i in province j; factor refers to the ratio of CO molecules to carbon atoms by weight; carbon emissions can be converted into CO 2 2 emissions by multiplying by this coefficient, E and E denote the electricity consumption ej hj and heat consumption in province j, respectively, d and d denote the carbon emission ej hj factor of electricity and heat consumption in province j. The carbon emissions generated during the use of public buildings are estimated by referring to the low level calorific value, carbon emission factor, and carbon oxidation rate provided by IPCC. Since the carbon emission factor of coal is not directly provided in IPCC, coal is considered as raw coal for calculation. The carbon emission factor of coal is 25.8 TC/TJ, the low calorific value is 20.908 GJ/T, and the carbon oxidation rate is 0.899. LNG is converted to natural gas volume for calculation, depending on its density as 0.42~0.46 g/cm . The average CO emission factors (kg-CO /kWh) of the national regional power grids 2 2 in 2011 and 2012, as queried by the NDRC and the Guidelines for Provincial Greenhouse Gas Inventories, are shown in Table 4. Table 4. Electricity carbon emission factors. Regional Grid Coverage of Provinces and Cities 2011 2012 Average Value North China Regional Grid Beijing, Tianjin, Heibei, Shanxi, Shandong, Western Inner Mongolia 0.8967 0.8843 0.8905 East China Regional Grid Liaoning, Jilin, Heilongjiang, Eastern Inner Mongolia 0.8189 0.7769 0.7979 Northeast Regional Grid Shanghai, Jiangsu, Zhejiang, Anhui, Fujian 0.7129 0.7035 0.7082 Central China Regional Grid Henan, Hubei, Hunan, Jiangxi, Sichuan, Chongqing 0.5955 0.5257 0.5606 Northwest Regional Grid Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang 0.6860 0.6671 0.67655 Southern Regional Grid Guangdong, Guangxi, Yunnan, Guizhou, Hainan 0.5748 0.5271 0.55095 Electricity carbon emission coefficients obtained based on public data query are gener- ally measured by the government or relevant departments in a unified manner, which is easily accessible, and their data source is authoritative. However, the data are not published annually, which is not conducive to the measurement of time series of building carbon emission data. The average value of these two years was used in this study. Inner Mongolia power emission factor is taken as the average value of 0.8442 in the east and west. Table 5 shows Coefficient Thermal CO emission [34]. 2. Index of green building Green buildings in China are classified as one-star, two-star, and three-star. Three stars are the highest level of green building. The index of green building is calculated through Equation (8). I GB = D 1 + D 2 + D 3 (8) 1 2 3 where D , D , D denote the number of one-star, two-star and three-star public green 1 2 3 buildings in China, respectively. Buildings 2022, 12, x FOR PEER REVIEW 7 of 16 is easily accessible, and their data source is authoritative. However, the data are not pub- lished annually, which is not conducive to the measurement of time series of building carbon emission data. The average value of these two years was used in this study. Inner Mongolia power emission factor is taken as the average value of 0.8442 in the east and west. Table 5 shows Coefficient Thermal CO2 emission [34]. Table 5. Coefficient Thermal CO2 emission (tCO2/MWh). Thermal CO2 Thermal CO2 Thermal CO2 Thermal CO2 Pro Buildings vinces 2022, 12, 424 Provinces and Provinces and Provinces and 7 of 16 Emission Coeffi- Emission Coef- Emission Coef- Emission Coef- and Cities Cities Cities Cities cient ficient ficient ficient Anhui 116 Guizhou 292 Hunan 110 Sichuan 105 Table 5. Coefficient Thermal CO emission (tCO /MWh). 2 2 Beijing 88 Hainan 57 Jilin 132 Tianjin 108 Fujian 112 Hebei 122 Jiangsu 109 Xinjiang 109 Thermal CO Thermal CO Thermal CO Thermal CO 2 2 2 2 Provinces Provinces and Provinces and Provinces and Emission Emission Emission Emission Gansu 110 Henan 124 Jiangxi 134 Yunnan 149 and Cities Cities Cities Cities Coefficient Coefficient Coefficient Coefficient Guangdong 93 Heilongjiang 155 Liaoning 130 Zhejiang 104 Anhui 116 Guizhou 292 Hunan 110 Sichuan 105 Guangxi 153 Hubei 122 Inner Mongolia 160 Chongqing 98 Beijing 88 Hainan 57 Jilin 132 Tianjin 108 Ningxia 120 Qinghai 245 Shandong 114 Shanxi 116 Fujian 112 Hebei 122 Jiangsu 109 Xinjiang 109 Gansu 110 Henan 124 Jiangxi 134 Yunnan 149 Shaanxi 149 Guangdong 93 Heilongjiang 155 Liaoning 130 Zhejiang 104 Guangxi 153 Hubei 122 Inner Mongolia 160 Chongqing 98 Ningxia 120 Qinghai 245 Shandong 114 Shanxi 116 2. Index of green building Shaanxi 149 Green buildings in China are classified as one-star, two-star, and three-star. Three stars are the highest level of green building. The index of green building is calculated 3. Industrial Structure through Equation (8). We use the percentage of added value of the tertiary industry to GDP to describe industrial structure [35]. IGB=× D 12 +D× +D×3 (8) 12 3 4. Empirical Results where 𝐷 , 𝐷 , 𝐷 denote the number of one-star, two-star and three-star public green 4.1. Spatial Distribution of Carbon Emission in Different Areas buildings in China, respectively. The regional energy balance of the China Energy Statistics Yearbook for 2009–2020 3. Industrial Structure was used to estimate the carbon emissions from the operation of public buildings in each province of the country using end-use energy consumption. The specific measurement We use the percentage of added value of the tertiary industry to GDP to describe results, in accordance with the previously stated zoning, are shown in Figure 1. industrial structure [35]. In general, the eastern region has more carbon emissions than the central, western, and northeastern regions. The top three provinces generating carbon emissions from public 4. Empirical Results buildings from 2008–2018 were Guangdong, Jiangsu, and Beijing. In 2019, Shandong 4.1. Spatial Distribution of Carbon Emission in Different Areas surpassed Beijing among the top three. Among the eastern regions, Guangdong Province has the most carbon emissions from The regional energy balance of the China Energy Statistics Yearbook for 2009–2020 public buildings and Hainan Province has the least. All provinces show an increasing trend was used to estimate the carbon emissions from the operation of public buildings in each year by year. Jiangsu Province and Hebei Province have a faster growth rate. In the central province of the country using end-use energy consumption. The specific measurement region, Henan Province is has the highest carbon emissions, except for 2017, and Jiangxi results, in accordance with the previously stated zoning, are shown in Figure 1. Province has the lowest carbon emissions. Other regions are steadily increasing, however, not as much as the vast majority of the eastern region’s emissions. HEN HUB SX GD BJ JS SH AH HUN JX SD ZJ HB FJ TJ HN 0 0 2009 2011 2013 2015 2017 2019 2007 2009 2011 2013 2015 2017 2019 Year Year (a) (b) Figure 1. Cont. Carbon emission of public buildings (Mt/co ) Carbon emission of public buildings (Mt/co ) 2 Buildings 2022, 12, x FOR PEER REVIEW 8 of 16 Buildings 2022, 12, 424 8 of 16 SC SAX NMG LN HLJ JL CQ GX XJ GS YN GZ 20 QH NX 15 15 10 10 2009 2011 2013 2015 2017 2019 2007 2009 2011 2013 2015 2017 2019 Year Year (c) (d) Figure 1. Carbon emission of public buildings in China among provinces during 2008 to 2019: (a) Figure 1. Carbon emission of public buildings in China among provinces during 2008 to Eastern Region; (b) Central Region; (c) Western Region; (d) Northeast Region. GD—Guangdong, 2019: (a) Eastern Region; (b) Central Region; (c) Western Region; (d) Northeast Region. BJ—Beijing, JS—Jiangsu, SH—Shanghai, SD—Shandong, ZJ—Zhejiang, HB—Hebei, FJ—Fujian, GD—Guangdong, BJ—Beijing, JS—Jiangsu, SH—Shanghai, SD—Shandong, ZJ—Zhejiang, HB— TJ—Tianjin, HN—Hainan, HEN—Henan, HUB—Hubei, SX—Shanxi, AH—Anhui, HUN—Hunan, Hebei, FJ—Fujian, TJ—Tianjin, HN—Hainan, HEN—Henan, HUB—Hubei, SX—Shanxi, AH—Anhui, JX—Jiangxi, SC—Sichuan, SAX—Shaanxi, NMG—Inner Mongolia, CQ—Chongqing, GX— HUN—Hunan, JX—Jiangxi, SC—Sichuan, SAX—Shaanxi, NMG—Inner Mongolia, CQ—Chongqing, Guangxi, XJ—Xinjiang, GS-Gansu, GZ—Guizhou, QH—Qinghai, NX—Ningxia, LN—Liaoning, GX—Guangxi, XJ—Xinjiang, GS-Gansu, GZ—Guizhou, QH—Qinghai, NX—Ningxia, LN—Liaoning, HLJ—Heilongjiang, JL—Jilin. HLJ—Heilongjiang, JL—Jilin. Among the western regions, Sichuan Province consistently has the highest carbon emis- In general, the eastern region has more carbon emissions than the central, western, sions from public buildings, and Qinghai Province steadily has the lowest. Chongqing fluc- and northeastern regions. The top three provinces generating carbon emissions from pub- tuated more, with carbon emissions decreasing in 2015 and 2017. The growth rate is larger lic buildings from 2008–2018 were Guangdong, Jiangsu, and Beijing. In 2019, Shandong in Inner Mongolia and Xinjiang. Carbon emissions in Inner Mongolia province were always surpassed Beijing among the top three. lower than Shaanxi province between 2008 and 2017, and exceeded Shaanxi province after Among the eastern regions, Guangdong Province has the most carbon emissions 2018. Xinjiang Province surpassed Chongqing, Yunnan Province, and Guangxi Province from public buildings and Hainan Province has the least. All provinces show an increas- in 2018. ing trend year by year. Jiangsu Province and Hebei Province have a faster growth rate. In In Northeast China, Liaoning Province has the highest carbon emissions from public the central region, Henan Province is has the highest carbon emissions, except for 2017, buildings. Carbon emissions from public buildings in Heilongjiang Province were higher than those in Jilin Province, except in 2012 and 2013. and Jiangxi Province has the lowest carbon emissions. Other regions are steadily increas- ing, however, not as much as the vast majority of the eastern region’s emissions. 4.2. Hot Spot Analysis of Carbon Emissions from Public Building Operations Among the western regions, Sichuan Province consistently has the highest carbon Hot spot analysis can reflect the spatial aggregation effect of carbon emissions from emissions from public buildings, and Qinghai Province steadily has the lowest. Chong- public buildings. The variation of the aggregation can be seen by plotting the hot spot and qing fluctuated more, with carbon emissions decreasing in 2015 and 2017. The growth rate cold spot areas in different years. The natural interruption point grading method was used is larger in Inner Mongolia and Xinjiang. Carbon emissions in Inner Mongolia province to classify the values of each year, calculated by Equations (1) and (2) into high, subhigh, were always lower than Shaanxi province between 2008 and 2017, and exceeded Shaanxi sublow, and low value cluster areas in order of largest to smallest. The study area of this province after 2018. Xinjiang Province surpassed Chongqing, Yunnan Province, and research is in the years 2008–2019, and the clustering results of 2010, 2013, 2016, and 2019 Gu ar angx e drawn i Pro equally vince in spaced 2018.for analysis, as shown in Figure 2. From Figure 2, it can be seen that evolution of provincial carbon emission clustering In Northeast China, Liaoning Province has the highest carbon emissions from public in China shows a spatially increasing trend from west to east. Overall, the high-value buildings. Carbon emissions from public buildings in Heilongjiang Province were higher clustering areas are mainly concentrated in Zhejiang, Jiangsu, Anhui, and Shanghai. The than those in Jilin Province, except in 2012 and 2013. low-value clustering areas are mainly concentrated in Qinghai and Sichuan. In 2016, the number of provinces with high-value clustering areas increased, then decreased in 2019. 4.2. Hot Spot Analysis of Carbon Emissions from Public Building Operations Over time, the high value cluster areas first expanded and then contracted. Hot spot analysis can reflect the spatial aggregation effect of carbon emissions from public buildings. The variation of the aggregation can be seen by plotting the hot spot and cold spot areas in different years. The natural interruption point grading method was used to classify the values of each year, calculated by Equations (1) and (2) into high, subhigh, sublow, and low value cluster areas in order of largest to smallest. The study area of this research is in the years 2008–2019, and the clustering results of 2010, 2013, 2016, and 2019 are drawn equally spaced for analysis, as shown in Figure 2. Carbon emission of public buildings (Mt/co ) Carbon emission of public buildings (Mt/co ) 2 Buildings 2022, 12, x FOR PEER REVIEW 9 of 16 Buildings 2022, 12, 424 9 of 16 (a) (b) (c) (d) Figure 2. Spatial agglomeration pattern of public building carbon emissions in China from 2008 to Figure 2. Spatial agglomeration pattern of public building carbon emissions in China from 2008 2019: (a) Spatial agglomeration pattern of public building carbon emissions in China in 2010; (b) to 2019: (a) Spatial agglomeration pattern of public building carbon emissions in China in 2010; Spatial agglomeration pattern of public building carbon emissions in China in 2013; (c) Spatial ag- (b) Spatial agglomeration pattern of public building carbon emissions in China in 2013; (c) Spatial glomeration pattern of public building carbon emissions in China in 2016; (d) Spatial agglomeration agglomeration pattern of public building carbon emissions in China in 2016; (d) Spatial agglomeration pattern of public building carbon emissions in China in 2019. pattern of public building carbon emissions in China in 2019. From Figure 2, it can be seen that evolution of provincial carbon emission clustering 4.3. Spatial Effects of the Influencing Factors of Public Buildings Carbon Emissions in China shows a spatially increasing trend from west to east. Overall, the high-value clus- Carbon emissions from public building operations are the dependent variable; pop- tering areas are mainly concentrated in Zhejiang, Jiangsu, Anhui, and Shanghai. The low- ulation, urbanization rate, industrial structure, GDP per capita, and green index are the value clustering areas are mainly concentrated in Qinghai and Sichuan. In 2016, the num- explanatory variables in the STIRPAT model; the time range is 2008–2019; and the X and ber of provinces with high-value clustering areas increased, then decreased in 2019. Over Y coordinates are the geographic coordinates of each province. With these factors, the time, the high value cluster areas first expanded and then contracted. runs can be entered into the spatio-temporal geographically weighted model to obtain the influence size of the five explanatory variables. In order to unify the comparison of 4.3. Spatial Effects of the Influencing Factors of Public Buildings Carbon Emissions influence size, the influence size values are arranged in descending order and divided by Carbon emissions from public building operations are the dependent variable; pop- equal spacing, and the positive and negative influence are divided into six levels. Positive ulation, urbanization rate, industrial structure, GDP per capita, and green index are the influence includes weak (WPI), medium (MPI), and strong positive influence (SPI), which explanatory variables in the STIRPAT model; the time range is 2008–2019; and the X and indicates that the influence factor positively contributes to the carbon emission of public Y coordinates are the geographic coordinates of each province. With these factors, the runs building operation; the negative influence includes weak (WNI), medium (MNI), and can be entered into the spatio-temporal geographically weighted model to obtain the in- strong negative influence (SNI), which indicates that the influence factor negatively inhibits fluence size of the five explanatory variables. In order to unify the comparison of influence the carbon emission of public building operation [36]. Due to the number of years, this size, the influence size values are arranged in descending order and divided by equal study selected 2010, 2013, 2016, and 2019 for spatial presentation and analysis. The GTWR spacing, and the positive and negati 2ve influence are divided into six levels. Positive influ- model was run with an adjusted R of 0.956 and an AICc of 199.253, indicating a better ence includes weak (WPI), medium (MPI), and strong positive influence (SPI), which in- model effect. dicates that the influence factor positively contributes to the carbon emission of public Based on the regression results of the GTWR model, the spatial and temporal variability building operation; the negative influence includes weak (WNI), medium (MNI), and of the five influencing factors of carbon emissions of public buildings is analyzed one by one. Buildings 2022, 12, x FOR PEER REVIEW 10 of 16 strong negative influence (SNI), which indicates that the influence factor negatively inhib- its the carbon emission of public building operation [36]. Due to the number of years, this study selected 2010, 2013, 2016, and 2019 for spatial presentation and analysis. The GTWR model was run with an adjusted R of 0.956 and an AICc of 199.253, indicating a better model effect. Buildings 2022, 12, 424 10 of 16 Based on the regression results of the GTWR model, the spatial and temporal varia- bility of the five influencing factors of carbon emissions of public buildings is analyzed one by one. 1. Spatial and temporal variation in the effect of population on carbon emissions 1. Spatial and temporal variation in the effect of population on carbon emissions Popul Population ation iis s a po a positive sitive ininfluence fluence on t on hethe carbon carbon emis emissions sions of pu of blic public build bu ing ildings s in each in each province. The maximum value of the population regression coefficient is 8.135 and province. The maximum value of the population regression coefficient is 8.135 and the m the inim minimum um valuvalue e is 0.08 is 2, 0.082, which c which an b can e dbe ividivided ded into into WPI, WPI, MPIMPI, , and S and PI SPI accor accor ding t ding o th to e the influence level. From the results, the provinces with the highest impact are Shanghai, influence level. From the results, the provinces with the highest impact are Shanghai, Zhejiang, Jiangsu, Shandong, Anhui, Tianjin, Beijing, and Fujian. Mainly with the growth of Zhejiang, Jiangsu, Shandong, Anhui, Tianjin, Beijing, and Fujian. Mainly with the growth population, the service industry activities in public buildings are frequent, thus increasing of population, the service industry activities in public buildings are frequent, thus increas- the carbon emissions from public buildings. As shown in Figure 3, the impact of population ing the carbon emissions from public buildings. As shown in Figure 3, the impact of pop- on carbon emissions from public buildings is increasing year by year. Spatially, the SPI of ulation on carbon emissions from public buildings is increasing year by year. Spatially, population size is gradually spreading from the northeast and eastern coastal regions to the the SPI of population size is gradually spreading from the northeast and eastern coastal central and western regions. By 2016, population size has reached a SPI within 30 provinces regions to the central and western regions. By 2016, population size has reached a SPI in the study area. within 30 provinces in the study area. Figure 3. Spatial distribution of the regression coefficients of population: (a) Spatial distribution of Figure 3. Spatial distribution of the regression coefficients of population: (a) Spatial distribution the regression coefficients of the population in 2010; (b) Spatial distribution of the regression coeffi- of the regression coefficients of the population in 2010; (b) Spatial distribution of the regression cients of the population in 2013; (c) Spatial distribution of the regression coefficients of the popula- coefficients of the population in 2013; (c) Spatial distribution of the regression coefficients of the tion in 2016; (d) Spatial distribution of the regression coefficients of the population in 2019. population in 2016; (d) Spatial distribution of the regression coefficients of the population in 2019. 2. Spatial and temporal variation in the effect of urbanization on carbon emissions 2. Spatial and temporal variation in the effect of urbanization on carbon emissions During the study period, the maximum value is 30.386 and the minimum value is During the study period, the maximum value is 30.386 and the minimum value is −8.649. According to the influence level, it can be divided into SNI, MNI, WNI, WPI, and 8.649. According to the influence level, it can be divided into SNI, MNI, WNI, WPI, and SPI. Overall, the urbanization rate has a predominantly negative impact on carbon emis- sions from public buildings. In 2010, WNI dominates, occupying 18 provinces, followed by WPI, occupying 11 provinces. In 2013, 2016, and 2019, WNI dominates, occupying 15 provinces, followed by WPI, occupying 14 provinces. It indicates that the urbaniza- tion rate has a small impact on the carbon emissions of public buildings. The scale of carbon emissions from public buildings does not increase with the increase in urbanization level; instead, it decreases with the optimization of energy consumption structure and the improvement of energy utilization efficiency. During the study period, the number of provinces with negative impact levels shows a decreasing trend, and spatially, the area Buildings 2022, 12, x FOR PEER REVIEW 11 of 16 SPI. Overall, the urbanization rate has a predominantly negative impact on carbon emis- sions from public buildings. In 2010, WNI dominates, occupying 18 provinces, followed by WPI, occupying 11 provinces. In 2013, 2016, and 2019, WNI dominates, occupying 15 provinces, followed by WPI, occupying 14 provinces. It indicates that the urbanization rate has a small impact on the carbon emissions of public buildings. The scale of carbon emissions from public buildings does not increase with the increase in urbanization level; instead, it decreases with the optimization of energy consumption structure and the im- Buildings 2022, 12, 424 11 of 16 provement of energy utilization efficiency. During the study period, the number of prov- inces with negative impact levels shows a decreasing trend, and spatially, the area of pos- itive effect gradually expands from the northeast and some southeastern provinces to the of positive effect gradually expands from the northeast and some southeastern provinces whole eastern region, and then gradually shifts to the central region and Xinjiang province to the whole eastern region, and then gradually shifts to the central region and Xinjiang (Figure 4). Public buildings are mostly located in urban areas and less in rural areas. The province (Figure 4). Public buildings are mostly located in urban areas and less in rural increase in urbanization rate will promote the development of tertiary industry, which areas. The increase in urbanization rate will promote the development of tertiary industry, will further promote the generation and operation of public buildings. When the urbani- which will further promote the generation and operation of public buildings. When the zation rate reaches a certain level, it will curb the carbon emissions of running public urbanization rate reaches a certain level, it will curb the carbon emissions of running public buildings because the stability of urbanization will make people start to raise the aware- buildings because the stability of urbanization will make people start to raise the awareness ness of energy saving, not only limited to the use of more focus on energy efficiency. of energy saving, not only limited to the use of more focus on energy efficiency. Figure 4. Spatial distribution of the regression coefficients of urbanization: (a) Spatial distribution Figure 4. Spatial distribution of the regression coefficients of urbanization: (a) Spatial distribution of of the regression coefficients of urbanization in 2010; (b) Spatial distribution of the regression coef- the regression coefficients of urbanization in 2010; (b) Spatial distribution of the regression coefficients ficients of urbanization in 2013; (c) Spatial distribution of the regression coefficients of urbanization of urbanization in 2013; (c) Spatial distribution of the regression coefficients of urbanization in 2016; in 2016; (d) Spatial distribution of the regression coefficients of urbanization in 2019. (d) Spatial distribution of the regression coefficients of urbanization in 2019. 3. Spatial and temporal variation in the effect of industrial structure on carbon emis- 3. Spatial and temporal variation in the effect of industrial structure on carbon emissions sions The regression coefficient of the industrial structure has a maximum value of 0.315 and The regression coefficient of the industrial structure has a maximum value of 0.315 a minimum value of 0.426 in the study period. Industrial structure refers to the ratio of and a minimum value of −0.426 in the study period. Industrial structure refers to the ratio the value added of the tertiary sector to the total value added of production. It is classified of the value added of the tertiary sector to the total value added of production. It is clas- as SPI, WPI, WNI, and MNI according to the influence level. As shown in Figure 5, it is sified as SPI, WPI, WNI, and MNI according to the influence level. As shown in Figure 5, generally a positive impact. 2010, 2013, 2016, and 2019 are dominated by weak positive it is generally a positive impact. 2010, 2013, 2016, and 2019 are dominated by weak positive impact, occupying 16, 21, 18, and 15 provinces, respectively. The positive effect of industrial structure is shifted from the east to the center. In 2010, the positive effect of industrial structure is in the eastern coastal region, northeastern region, and Xinjiang province, and that positive effect is partially shifted to the central region in 2019. Buildings 2022, 12, x FOR PEER REVIEW 12 of 16 impact, occupying 16, 21, 18, and 15 provinces, respectively. The positive effect of indus- trial structure is shifted from the east to the center. In 2010, the positive effect of industrial Buildings 2022, 12, 424 12 of 16 structure is in the eastern coastal region, northeastern region, and Xinjiang province, and that positive effect is partially shifted to the central region in 2019. Figure 5. Spatial distribution of the regression coefficients of industrial structure: (a) Spatial distri- Figure 5. Spatial distribution of the regression coefficients of industrial structure: (a) Spatial dis- bution of the regression coefficients of industrial structure in 2010; (b) Spatial distribution of the tribution of the regression coefficients of industrial structure in 2010; (b) Spatial distribution of regression coefficients of industrial structure in 2013; (c) Spatial distribution of the regression coef- the regression coefficients of industrial structure in 2013; (c) Spatial distribution of the regression ficients of industrial structure in 2016; (d) Spatial distribution of the regression coefficients of indus- coefficients of industrial structure in 2016; (d) Spatial distribution of the regression coefficients of trial structure in 2019. industrial structure in 2019. 4. Spatial and temporal variation in the effect of GDP per capita on carbon emissions 4. Spatial and temporal variation in the effect of GDP per capita on carbon emissions The maximum value of the regression coefficient for GDP per capita is 1.177 and the The maximum value of the regression coefficient for GDP per capita is 1.177 and minimum value is −1.184. In 2010, the positive effect is dominant, with a total of 16 prov- the minimum value is 1.184. In 2010, the positive effect is dominant, with a total of inces in WPI and MPI. In 2013, the negative effect is dominant, with a total of 22 provinces 16 provinces in WPI and MPI. In 2013, the negative effect is dominant, with a total of in SNI, MNI, and WNI. In 2016, the positive effect is more pronounced, with 24 provinces 22 provinces in SNI, MNI, and WNI. In 2016, the positive effect is more pronounced, with in WPI. In 2019, the positive effect is pronounced, with a total of 22 provinces in WPI and 24 provinces in WPI. In 2019, the positive effect is pronounced, with a total of 22 provinces MPI. As shown in Figure 6, the influence of GDP per capita shows a trend from positive in WPI and MPI. As shown in Figure 6, the influence of GDP per capita shows a trend from to negative and then positive again. By 2019, the only regions with a negative effect are positive to negative and then positive again. By 2019, the only regions with a negative effect Buildings 2022, 12, x FOR PEER REVIEW 13 of 16 Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Hubei, and Hunan. It indicates that are Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Hubei, and Hunan. It indicates that population affluence is promoting the increase in carbon emissions from public buildings. population affluence is promoting the increase in carbon emissions from public buildings. Figure 6. Cont. Figure 6. Spatial distribution of the regression coefficients of GDP per capita: (a) Spatial distribution of the regression coefficients of GDP per capita in 2010; (b) Spatial distribution of the regression coefficients of GDP per capita in 2013; (c) Spatial distribution of the regression coefficients of GDP per capita in 2016; (d) Spatial distribution of the regression coefficients of GDP per capita in 2019. 5. Spatial and temporal variation in the effect of the green building index on carbon emissions The maximum value of the regression coefficient of the green building index is 0.832 and the minimum value is −0.098. As shown in Figure 7, in 2010, all 30 provinces have a positive effect. In 2013, the WNI dominated, occupying 16 provinces. In 2016, WPI occu- pied 17 provinces. In 2019, WPI occupies 21 provinces. In 2010, the green building index has a positive effect on carbon emissions from public buildings in all regions. However, by 2019, the inhibitory effect has 9 provinces. According to previous studies, green build- ings contribute to carbon emission reduction than non-green buildings, but this study finds that the large-scale effect of carbon emission reduction from green buildings has not yet been developed. Buildings 2022, 12, x FOR PEER REVIEW 13 of 16 Buildings 2022, 12, x FOR PEER REVIEW 13 of 16 Buildings 2022, 12, 424 13 of 16 Figure 6. Spatial distribution of the regression coefficients of GDP per capita: (a) Spatial distribution of the regression coefficients of GDP per capita in 2010; (b) Spatial distribution of the regression Figure 6. Spatial distribution of the regression coefficients of GDP per capita: (a) Spatial distribution Figure 6. Spatial distribution of the regression coefficients of GDP per capita: (a) Spatial distribution coefficients of GDP per capita in 2013; (c) Spatial distribution of the regression coefficients of GDP of the regression coefficients of the regression coefficients of GDP per ca of GDP per capita pita in 2010; in 2010; ( (b b) Spatial ) Spatial di distribution stribution of the regression of the regression per capita in 2016; (d) Spatial distribution of the regression coefficients of GDP per capita in 2019. coefficients of GDP per capita in 2013; (c) Spatial distribution of the regression coefficients of GDP coefficients of GDP per capita in 2013; (c) Spatial distribution of the regression coefficients of GDP per capita in 2016; (d) Spatial distribution of the regression coefficients of GDP per capita in 2019. per 5. capita Spat inia2016; l and t (de ) m Spatial poral v distribution ariation in t of the he e regr ffect ession of th coef e gr ficients een buof ildin GDP g index on ca per capita inr2019. bon emissions 5. Spatial and temporal variation in the effect of the green building index on carbon 5. Spatial and temporal variation in the effect of the green building index on carbon emissions The maximum value of the regression coefficient of the green building index is 0.832 emissions and the minimum value is −0.098. As shown in Figure 7, in 2010, all 30 provinces have a The maximum value of the regression coefficient of the green building index is 0.832 The maximum value of the regression coefficient of the green building index is 0.832 an posi d th tieve ef minfect. In 20 imum va13 lu, the WNI e is 0.098 domi . Asnated, shown occupyi in Figu nr g 16 e 7, prov in 20i1 n0 ces. In , all 30 20 p16 ro, WPI occ vinces ha u v -e a and the minimum value is −0.098. As shown in Figure 7, in 2010, all 30 provinces have a pied 17 provinces. In 2019, WPI occupies 21 provinces. In 2010, the green building index positive effect. In 2013, the WNI dominated, occupying 16 provinces. In 2016, WPI occupied posi has tive ef a positive effect on c fect. In 2013, the WNI arbon emission dominated, s from occupyi public buildings ng 16 prov in inall re ces. In gion 20s. H 16, WPI occ owever, u- 17 provinces. In 2019, WPI occupies 21 provinces. In 2010, the green building index has a piby 201 ed 17 provinces. In 201 9, the inhibitory eff 9, WPI occupies ect has 9 provinces. 21 province Accordins. In g to prev 2010 iou , th se st green udies, bu gre ild en bu ing ind ild- ex positive effect on carbon emissions from public buildings in all regions. However, by 2019, the ings contribute to carbon emission reduction than non-green buildings, but this study has a positive effect on carbon emissions from public buildings in all regions. However, inhibitory effect has 9 provinces. According to previous studies, green buildings contribute to finds that the large-scale effect of carbon emission reduction from green buildings has not by 2019, the inhibitory effect has 9 provinces. According to previous studies, green build- carbon emission reduction than non-green buildings, but this study finds that the large-scale yet been developed. ings contribute to carbon emission reduction than non-green buildings, but this study effect of carbon emission reduction from green buildings has not yet been developed. finds that the large-scale effect of carbon emission reduction from green buildings has not yet been developed. Buildings 2022, 12, x FOR PEER REVIEW 14 of 16 Figure 7. Spatial distribution of the regression coefficients of the green building index: (a) Spatial Figure 7. Spatial distribution of the regression coefficients of the green building index: (a) Spatial distribution of the regression coefficients of the green building index in 2010; (b) Spatial distribution distribution of the regression coefficients of the green building index in 2010; (b) Spatial distribution of of the regression coefficients of the green building index in 2013; (c) Spatial distribution of the re- the regression coefficients of the green building index in 2013; (c) Spatial distribution of the regression gression coefficients of the green building index in 2016; (d) Spatial distribution of the regression coefficients of the green building index in 2016; (d) Spatial distribution of the regression coefficients coefficients of the green building index in 2019. of the green building index in 2019. 5. Conclusions and Policy Suggestions 5.1. Conclusions Using the carbon emission of public buildings in operation in China from 2008 to 2019, the GTWR model was used to detect the spatial distribution of the influence coeffi- cients of population, urbanization, industrial structure, GDP per capita, and index of green buildings. We found significant spatial heterogeneity changes between the five fac- tors. Most importantly, the factor of the index of green buildings. The empirical results of the green building index and carbon emissions of public building operation help to determine the direction and intensity of green building devel- opment, and help to enrich the study of the impact of green buildings on the carbon emis- sions of public buildings from a regional perspective. In terms of carbon emissions from public building operations, the top three public building carbon emissions from 2008–2018 were Guangdong, Jiangsu, and Beijing, and in 2019, Shandong surpassed Beijing among the top three. Overall, the total carbon emissions from public buildings in the eastern region, except Hainan, Fujian, and Tianjin, are greater than those in the central, western, and northeastern regions, and the growth rate is obvi- ous. The results of the hotspot analysis show that there are east-west differences in the operational carbon emissions of public buildings in Chinese provinces. The evolution of clustering shows a spatially increasing trend from west to east. The STIRPAT model shows that population has a positive influence on public build- ing carbon emissions in each province, and the positive influence of population gradually spreads from the northeast and the eastern coastal regions to the central and western re- gions; urbanization rate has a predominantly negative influence on public building car- bon emissions; industrial structure has a positive influence; the influence of GDP per cap- ita and green building index shows a trend of positive to negative and then positive; the large-scale effect of green building carbon emission reduction has not yet been formed. 5.2. Suggestions According to the above conclusions, we can make the following recommendations. First, total carbon emission control should be carried out at the regional level under the constraints of other indicators such as socio-economic development rate and industry economic growth. Focus on controlling carbon emission hotspot areas and emission re- duction measures should be formulated for cold spot areas, according to development Buildings 2022, 12, 424 14 of 16 5. Conclusions and Policy Suggestions 5.1. Conclusions Using the carbon emission of public buildings in operation in China from 2008 to 2019, the GTWR model was used to detect the spatial distribution of the influence coefficients of population, urbanization, industrial structure, GDP per capita, and index of green buildings. We found significant spatial heterogeneity changes between the five factors. Most importantly, the factor of the index of green buildings. The empirical results of the green building index and carbon emissions of public build- ing operation help to determine the direction and intensity of green building development, and help to enrich the study of the impact of green buildings on the carbon emissions of public buildings from a regional perspective. In terms of carbon emissions from public building operations, the top three public building carbon emissions from 2008–2018 were Guangdong, Jiangsu, and Beijing, and in 2019, Shandong surpassed Beijing among the top three. Overall, the total carbon emissions from public buildings in the eastern region, except Hainan, Fujian, and Tianjin, are greater than those in the central, western, and northeastern regions, and the growth rate is obvious. The results of the hotspot analysis show that there are east-west differences in the operational carbon emissions of public buildings in Chinese provinces. The evolution of clustering shows a spatially increasing trend from west to east. The STIRPAT model shows that population has a positive influence on public building carbon emissions in each province, and the positive influence of population gradually spreads from the northeast and the eastern coastal regions to the central and western regions; urbanization rate has a predominantly negative influence on public building carbon emissions; industrial structure has a positive influence; the influence of GDP per capita and green building index shows a trend of positive to negative and then positive; the large-scale effect of green building carbon emission reduction has not yet been formed. 5.2. Suggestions According to the above conclusions, we can make the following recommendations. First, total carbon emission control should be carried out at the regional level under the constraints of other indicators such as socio-economic development rate and industry economic growth. Focus on controlling carbon emission hotspot areas and emission reduc- tion measures should be formulated for cold spot areas, according to development needs to avoid generating large amounts of carbon emissions due to rapid economic development. Cooperation between provinces can be strengthened to develop inter-provincial carbon emission trading policies to balance provincial carbon emissions. Second, for provinces with large populations, public buildings should be retrofitted with energy efficiency. It would be advisable while developing the economy to adjust the energy consumption structure, change the economic development mode, adhere to the path of low carbon development, and slow down the rapid growth of carbon emissions caused by the rapid growth of the regional economic development level. Third, the development of green buildings still needs to continue to improve, and the current growth of green buildings has not yet formed a scale effect on the carbon emission aspect of public buildings. Because of the high cost of green building construction, there is a need to support the construction and development of green buildings in economically disadvantaged areas. However, this study is subject to several limitations. Firstly, this paper uses China as an example, and the analysis for green buildings can be extended to other emerging economies and developing countries. Secondly, temperature has a different impact on the heating and cooling of public buildings under different climate backgrounds. Further study can take a closer look at the micro-level of the impact of temperature on the energy consumption of public buildings. Finally, the empirical results of this paper are helpful for policy makers to develop differentiated emission reduction strategies for high and low carbon emission provincial administrative regions. Buildings 2022, 12, 424 15 of 16 Author Contributions: Conceptualization, Z.D. and Y.L.; methodology, Z.D. and Y.L.; validation, Z.D.; formal analysis, Z.D.; data curation, Z.D. and Z.Z.; writing—original draft preparation, Z.D.; writing—review and editing, Z.D.; visualization, Z.D. and Y.L.; project administration, Z.D. and Z.Z.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by National Natural Science Foundation of China, grant number 71871014. Data Availability Statement: The data used to support the results in this article are included within the paper. In addition, some of the data in this paper are supported by the references mentioned in the manuscript. 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