Eco-Efficiency Assessment of Beijing-Tianjin-Hebei Urban Agglomeration Based on Emergy Analysis and Two-Layer System Dynamics

Huanhuan Huo; Haiyan Liu; Xinzhong Bao; Wei Cui

doi:10.3390/systems10030061

Eco-Efficiency Assessment of Beijing-Tianjin-Hebei Urban Agglomeration Based on Emergy Analysis and Two-Layer System Dynamics

Huo, Huanhuan;Liu, Haiyan;Bao, Xinzhong;Cui, Wei 2022-05-08 00:00:00 systems Article Eco-Efﬁciency Assessment of Beijing-Tianjin-Hebei Urban Agglomeration Based on Emergy Analysis and Two-Layer System Dynamics 1 1 , 2 1 Huanhuan Huo , Haiyan Liu *, Xinzhong Bao and Wei Cui School of Economics and Management, China University of Geosciences (Beijing), Beijing 100083, China; huohh@cugb.edu.cn (H.H.); cuiw@cugb.edu.cn (W.C.) Management College, Beijing Union University, Beijing 100101, China; baoxz@buu.edu.cn * Correspondence: liuhy@cugb.edu.cn; Tel.: +86-010-82321343 Abstract: In the process of the economic development of the Beijing-Tianjin-Hebei urban agglom- eration, ecological and environmental issues are still an important factor restricting high-quality development. The study of eco-efﬁciency is of great signiﬁcance for coordinating the relationship between economy, resources and environment. This paper used a combinated method of two-layer system dynamics and emergy analysis to construct an emergy–system dynamics coupling model for eco-efﬁciency evaluation from the subsystems of resource ﬂow, energy ﬂow, currency ﬂow and population ﬂow of urban system, which is used to simulate and analyze the eco-efﬁciency of the Beijing-Tianjin-Hebei urban agglomeration. The results show that the overall eco-efﬁciency of the Beijing-Tianjin-Hebei urban agglomeration is not high, with an average value of 0.3786, and there is a trend of the value rising ﬁrst and then falling from 2000 to 2035. The index values of emergy waste rate, contaminant emergy ratio, emergy output rate and environmental load rate after the Citation: Huo, H.; Liu, H.; Bao, X.; decomposition of the eco-efﬁciency show that the high environmental pressure, the low re-use rate Cui, W. Eco-Efﬁciency Assessment of of pollutants and the low production efﬁciency of the system are important reasons for the low Beijing-Tianjin-Hebei Urban eco-efﬁciency in regional economic development. Finally, through scenario simulation, we propose Agglomeration Based on Emergy Analysis and Two-Layer System that optimizing the economic structure, adjusting the population size and rationally arranging the Dynamics. Systems 2022, 10, 61. ﬁxed assets investment are conducive to improving the eco-efﬁciency of the Beijing-Tianjin-Hebei https://doi.org/10.3390/ urban agglomeration. systems10030061 Keywords: eco-efﬁciency; two-layer system dynamics; emergy analysis; Beijing-Tianjin-Hebei ur- Academic Editors: Jinan Fiaidhi, ban agglomeration Aboul Ella Hassanien and Hye-jin Kim Received: 28 March 2022 Accepted: 30 April 2022 1. Introduction Published: 8 May 2022 Under the guidance of the national integration policy and the promotion of the urban Publisher’s Note: MDPI stays neutral upgrading, urban agglomerations are gradually becoming a new carrier of economic de- with regard to jurisdictional claims in velopment. High-quality development of urban agglomerations can optimize the regional published maps and institutional afﬁl- development pattern and drive the high-quality development of the whole economy [1]. iations. The Beijing-Tianjin-Hebei urban agglomeration is an important part of China’s core area, the outline of Beijing-Tianjin-Hebei Coordinated Development Planning issued in 2015 clearly stated that “by 2035, the structure and regional integration pattern of the Beijing-Tianjin- Hebei world-class urban agglomeration will be basically formed, the regional economic Copyright: © 2022 by the authors. structure will be more reasonable, and the quality of the ecological environment will be Licensee MDPI, Basel, Switzerland. generally well”. In 2021, The 14th Five-Year Plan for the National Economic and Social This article is an open access article Development of the People’s Republic of China and the Outline of the Vision for 2035 even distributed under the terms and put “accelerating the coordinated development of Beijing, Tianjin and Hebei” at the top conditions of the Creative Commons of the “in-depth implementation of major regional strategies”, and listed it as the “ﬁrst Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ echelon of accelerating high-quality development” nationwide. High-quality development 4.0/). means that the work of ecological and environmental protection cannot be ignored in the Systems 2022, 10, 61. https://doi.org/10.3390/systems10030061 https://www.mdpi.com/journal/systems Systems 2022, 10, 61 2 of 17 process of economic development. Therefore, quantifying and coordinating the relationship between economic development and resources and the environment of the Beijing-Tianjin- Hebei urban agglomeration is of great signiﬁcance in order to promote the high-quality development of the Beijing-Tianjin-Hebei region. The research on the economic development and ecological environment of the Beijing- Tianjin-Hebei region focuses on the impact of economic development on the ecological environment. For example, He and Cai measure and analysis the degree of decoupling degree between economic growth and environmental resources in the Beijing-Tianjin-Hebei region, and ﬁnd that the rapid economic development in the Beijing-Tianjin-Hebei region has not fully realize the negative growth of resource consumption, as well as resource utilization rate and economical utilization rate are still at a low level [2]. Wang ﬁnd that the coupling and coordinated development between economic society and ecolog- ical environment of the Beijing-Tianjin-Hebei urban agglomeration presented dynamic evolution (represented as S-shaped), showing an overall upward trend, and the growth mode gradually changed from the economic growth lag to the ecological environment lag [3]. Zhang et al. use the panel data approach (PDA) to examine the causal impact of the Beijing-Tianjin-Hebei strategy on Hebei’s economy and environment under a counterfac- tual framework. The main ﬁnding is that the Beijing-Tianjin-Hebei strategy signiﬁcantly increases the proportion of Hebei’s tertiary industry in GDP and signiﬁcantly reduces the geographic average PM2.5 concentration, but it has no signiﬁcant impact on Hebei’s GDP growth rate [4]. Xue and Zhou use the DDF-GML index to measure the green total factor productivity in the Beijing-Tianjin-Hebei region from 2005 to 2018, and found that there were “low growth” and “unbalanced” problems in the green total factor productivity during the sample period [5]. To sum up, we can see that the environmental quality of the Beijing-Tianjin-Hebei region has been improved in the process of economic development, but low energy utilization efﬁciency and environmental problems are still important factors restricting high-quality development. Eco-efﬁciency as a comprehensive index reﬂecting the situation of economic, resource and environmental [6], the evaluation of urban eco-efﬁciency can objectively evaluate the efﬁciency relationship between the overall resource allocation, environmental quality and economic development of a city, so as to guide the coordinated and sustainable development of cities [7]. Some scholars adopted the single ratio method [8], the emergy value (or material ﬂow) account accounting method [9], the index system method [10] and the model method (including data envelopment analysis (DEA) and stochastic frontier analysis method (SFA)) [11,12] to explore the level and spatial differences of eco-efﬁciency in cities and urban agglomerations. Due to the advantages of using fewer indexes and the fact that it can directly process the indexes of different dimensions, DEA is widely used in efﬁciency evaluation in various ﬁelds. For example, Gai and Zhan use the SBM model that considers the undesired output to measure the marine eco-efﬁciency of China’s coastal provinces, and they describe the evolution characteristics of the spatial pattern with the help of the center of gravity model [13]. Tu et al. use super-efﬁciency (SBM) and Malmquist index to measure the eco-efﬁciency of the Pearl River Delta urban agglomeration from both static and dynamic aspects [14]. Zhang et al. use the Super-SBM model with unexpected outputs and standard deviation ellipses to study the dynamic changes and spatiotemporal differences of urban eco-efﬁciency in the lower Yellow River [15]. However, the calculation of eco-efﬁciency based on DEA model regards the region as a “black box”, which cannot reﬂect the internal structure of the regional eco-economic system, and does not take into account the interaction between its internal subsystems. With the development of social economy and the improvement of management prac- tice ability, it is required to open the efﬁciency evaluation “black box” and deeply under- stand the interior of the decision-making unit. System dynamics (SD) [16] emphasizes the consideration of the problem as a hole, and understands the composition of the problem and the interaction between various parts, as well as using dynamic simulation to investi- gate the dynamic change behavior and development trend of the system [17]. Its essence is Systems 2022, 10, 61 3 of 17 to open the “black box” of the decision-making unit, decompose the complex system, and investigate the inﬂuence of each link on the overall efﬁciency of the system. Currently, it has been applied to study the interaction relation between environmental and economic factors [18], sustainable development strategy research of highway systems [19], and the evaluation of regional circular economy [20], and so on. However, with the strengthening of the ﬂowing role of economic, resource and other factors in the global and regional urban networks, isolated point-like cities gradually evolve into closely interconnected planar urban agglomerations [21]. Urban agglomerations are interconnected by the elements of population, resources and economy within the urban agglomeration. Therefore, based on the existing studies [22,23], this paper introduces two-layer system dynamics (the ﬁrst layer is the spatial layout of the urban agglomeration, namely the Beijing, Tianjin and Hebei province, and the second layer is the relationship of urban internal factors) to evaluate the eco-efﬁciency of the Beijing-Tianjin-Hebei urban agglomeration. The eco-efﬁciency evaluation system based on system dynamics is a complex system integrating economic, social and environmental factors. These factors interact with each other, but it is difﬁcult to conduct a comprehensive and consistent analysis of the interaction between them due to the different equivalents. Emergy analysis (EMA) was ﬁrst proposed by Odum in the 1980s [24]. It can convert all types of resources (whether energy or matter) into one form of energy, namely solar energy, that makes it possible to study various types of materials, energy and capital in one system [25]. In addition, emergy-based indicators such as emergy output rate, emergy load rate, and eco-efﬁciency index are directly linked to urban ecosystems in an integrated way by incorporating service value [26], that can reﬂect environmental pressure, eco-efﬁciency, changes in energy structure, and resource utilization, etc. Therefore, this method has been widely applied to the sustainability evaluation of urban circular economy [27], industrial ecosystems [9] and regional economic systems [28]. In this paper we combine it with system dynamics to make up for the deﬁciency of different equivalent of system dynamics. Based on the perspective of functional ﬂow, this paper takes the Beijing-Tianjin-Hebei urban agglomeration as the research object, constructing an emergy-SD coupling model for eco-efﬁciency evaluation by the method of emergy analysis and the system dynamics. Finally, the eco-efﬁciency of the Beijing-Tianjin-Hebei urban agglomeration is evaluated, and the trend of eco-efﬁciency under different scenarios is discussed, which provides scientiﬁc reference for the effective implementation of urban development strategy. Next, we introduce the research ﬁelds and data sources. Then, the framework of urban agglomeration eco-efﬁciency evaluation is given and the developed model and its formula, calculation method, parameters and evaluation index are explained. Finally, we will verify the model and analyze the simulation results. 2. Research Objects and Data Sources The Beijing-Tianjin-Hebei urban agglomeration takes the capital Beijing and municipality Tianjin as the center, and other major cities include Shijiazhuang, Baoding, Langfang, Handan, etc. (as shown in Figure 1). Statistics in 2020 show that in the Beijing-Tianjin-Hebei region, Beijing hosts 20.37% of the resident population on 7.6% of the land, creating 41.78% of the regional output value; 5.5% of the land in Tianjin hosts 12.90% of the resident population and creates 16.32% of the regional output value. Of the land in in the Hebei province, 86.9% bears 66.73% of the permanent population and creates 41.89% of the regional output value. At the beginning of this century, scholar Wu Liangyong proposed the Greater Beijing Plan, which is usually regarded as the beginning of the integration of Beijing, Tianjin and Hebei. After that, the coordinated development of Beijing, Tianjin and Hebei has experienced three stages, namely, the three regions reached consensus on the cooperation of the Beijing-Tianjin-Hebei region, the initial formulation of regional development planning, and the coordinated development of the Beijing-Tianjin-Hebei region was elevated to a national strategy level and its implementation was accelerated. Recently, Beijing issued the “Implementation Plan on Establishing a More Effective New Mechanism for Coordinated Systems 2022, 10, 61 4 of 17 Regional Development”, which proposed that by 2035, the framework of Beijing-Tianjin- Hebei world-class urban agglomeration will be basically formed. Therefore, this paper takes 2000–2035 as the research period. The following basic data were used: the total population of Beijing, Tianjin and Hebei Province are 1382, 1001.14 and 6674 million, respectively. The emergy data of Beijing-Tianjin-Hebei resource stock are calculated by emergy analysis. Systems 2022, 10, x FOR PEER REVIEW 4 of 19 Updatable resources include solar energy, wind energy, rainwater chemical energy and potential energy, which provides driving forces for the ecological economic system. Unable to update resources include oil, natural gas, etc. At the same time, the area also imports goods and equipment from the outside, and exports sewage and garbage to the outside. Figure 1. Beijing-Tianjin-Hebei urban agglomeration. Figure 1. Beijing-Tianjin-Hebei urban agglomeration. The original data used in this study came from China Statistical Yearbook, China At the beginning of this century, scholar Wu Liangyong proposed the Greater Beijing Energy Statistical Yearbook, Beijing Statistical Yearbook, Tianjin Statistical Yearbook, Hebei Plan, which is usually regarded as the beginning of the integration of Beijing, Tianjin and Statistical Yearbook, Regional Statistical Yearbook, National Economic and Social Devel- Hebei. After that, the coordinated development of Beijing, Tianjin and Hebei has experi- opment Statistical Bulletin, China’s economic and social big data research platform, etc. enced three stages, namely, the three regions reached consensus on the cooperation of the The energy statistical yearbook reﬂects China’s energy construction, production, consump- Beijing-Tianjin-Hebei region, the initial formulation of regional development planning, tion, and the balance between supply and demand. The statistical yearbooks of various and the coordinated development of the Beijing-Tianjin-Hebei region was elevated to a provinces and cities reﬂect the annual data of local economic and social development. These national strategy level and its implementation was accelerated. Recently, Beijing issued data are mainly derived from the census, and are veriﬁed and corrected in comparison the “Implementation Plan on Establishing a More Effective New Mechanism for Coordi- with historical data, and they have a certain reliability. nated Regional Development”, which proposed that by 2035, the framework of Beijing- 3. System Dynamics Method Tianjin-Hebei world-class urban agglomeration will be basically formed. Therefore, this paper 3.1. tak Framework es 2000–20of 35the as t Model he research period. The following basic data were used: the total population of Beijing, Tianjin and Hebei Province are 1382, 1001.14 and 6674 million, re- The urban eco-efﬁciency evaluation system may contain several subsystems, which spectively. The emergy data of Beijing-Tianjin-Hebei resource stock are calculated by may be included in a larger system (urban agglomeration or country). Therefore, we emergy analysis. Updatable resources include solar energy, wind energy, rainwater chem- established a two-layer system dynamics model to study the eco-efﬁciency of urban ag- ical energy and potential energy, which provides driving forces for the ecological eco- glomerations. In this model, the ﬁrst layer is the spatial layout of the urban agglomeration. nomic system. Unable to update resources include oil, natural gas, etc. At the same time, Each urban system within the urban agglomeration is regarded as an element in the system, the area also imports goods and equipment from the outside, and exports sewage and so as to realize the overall analysis. The second layer is the smaller scale—urban scale. In garbage to the outside. addition to the inﬂuence of factors within the subsystem, there is population ﬂow among subsystems, The original as da shown ta used in in Figur thise s2 t.udy came from China Statistical Yearbook, China Energy Statistical Yearbook, Beijing Statistical Yearbook, Tianjin Statistical Yearbook, He- bei Statistical Yearbook, Regional Statistical Yearbook, National Economic and Social De- velopment Statistical Bulletin, China’s economic and social big data research platform, etc. The energy statistical yearbook reflects China’s energy construction, production, con- sumption, and the balance between supply and demand. The statistical yearbooks of var- ious provinces and cities reflect the annual data of local economic and social development. These data are mainly derived from the census, and are verified and corrected in compar- ison with historical data, and they have a certain reliability. Systems 2022, 10, x FOR PEER REVIEW 5 of 19 3. System Dynamics Method 3.1. Framework of the Model The urban eco-efficiency evaluation system may contain several subsystems, which may be included in a larger system (urban agglomeration or country). Therefore, we es- tablished a two-layer system dynamics model to study the eco-efficiency of urban agglom- erations. In this model, the first layer is the spatial layout of the urban agglomeration. Each urban system within the urban agglomeration is regarded as an element in the system, so as to realize the overall analysis. The second layer is the smaller scale—urban scale. In Systems 2022, 10, 61 5 of 17 addition to the influence of factors within the subsystem, there is population flow among subsystems, as shown in Figure 2. Figure 2. Framework description of the two-layer model of system dynamics. Figure 2. Framework description of the two-layer model of system dynamics. Some scholars have studied the inﬂuencing factors of eco-efﬁciency. Ou uses the Some scholars have studied the influencing factors of eco-efficiency. Ou uses the spa- spatial error model (SEM) to study the inﬂuencing factors of eco-efﬁciency, and found tial error model (SEM) to study the influencing factors of eco-efficiency, and found that that factors such as environmental regulation, economic development level, structural factors such as environmental regulation, economic development level, structural changes, opening to the outside world and urbanization all have a signiﬁcant impact changes, opening to the outside world and urbanization all have a significant impact on on eco-efﬁciency [29]. Qu uses the spatial lag model (SLM) to analyze the inﬂuencing eco-efficiency [29]. Qu uses the spatial lag model (SLM) to analyze the influencing factors factors of regional eco-efﬁciency. The results show that the regional economic development of regional eco-efficiency. The results show that the regional economic development level, level, state-owned proportion, foreign investment and R&D intensity have a positive effect state-owned proportion, foreign investment and R&D intensity have a positive effect on on the improvement of eco-efﬁciency level, while the increase in capital–labor ratio and the improvement of eco-efficiency level, while the increase in capital–labor ratio and the the proportion of export trade are not conducive to the improvement of eco-efﬁciency proportion of export trade are not conducive to the improvement of eco-efficiency level level [30]. Chen et al. use the spatial panel econometric model to explore the impact [30]. Chen et al. use the spatial panel econometric model to explore the impact of tourism of tourism economic development on regional eco-efﬁciency and its spatial effect. It is economic development on regional eco-efficiency and its spatial effect. It is found that in found that in the long-term development, tourism economic development and regional the long-term development, tourism economic development and regional eco-efficiency eco-efﬁciency shows a relatively obvious “Kuznets Curve” effect [31]. Tang et al. construct shows a relatively obvious “Kuznets Curve” effect [31]. Tang et al. construct a macroeco- a macroeconomic model with output loss and innovation compensation factors to prove nomic model with output loss and innovation compensation factors to prove that land that land urbanization has a negative impact on urban eco-efﬁciency, and the improvement urbanization has a negative impact on urban eco-efficiency, and the improvement of in- of industrial structure plays a positive mediating role between the two [32]. To sum up, dustrial structure plays a positive mediating role between the two [32]. To sum up, exist- existing studies have found that economic development level, industrial structure, urban ing studies have found that economic development level, industrial structure, urban pop- population size and density, energy structure, government environmental regulation and ulation size and density, energy structure, government environmental regulation and for- foreign direct investment have an impact on eco-efﬁciency. eign direct investment have an impact on eco-efficiency. This paper analyzes the inﬂuencing factors of eco-efﬁciency system with reference This paper analyzes the influencing factors of eco-efficiency system with reference to to the conceptual framework “Driving-Force-Pressure-State-Impact-Response” (DPSIR) the conceptual framework “Driving-Force-Pressure-State-Impact-Response” (DPSIR) rec- recommended by the United Nations Environment Programme (UNEP). The main “driving ommended by the United Nations Environment Programme (UNEP). The main “driving factors” affecting the eco-efﬁciency system of urban agglomerations are total change in factors” affecting the eco-efficiency system of urban agglomerations are total change in economy and population. The evaluation of eco-efﬁciency is mainly through the mea- economy and population. The evaluation of eco-efficiency is mainly through the measure- surement of “status and impact” indicators such as economic quality, resource supply, ment of “status and impact” indicators such as economic quality, resource supply, and and environmental impact. The “response” in the DPSIR framework mainly refers to environmental impact. The “response” in the DPSIR framework mainly refers to the fact the fact that decision-makers adjust policies and management methods and optimize the that decision-makers adjust policies and management methods and optimize the interaction of economy, society and environment by changing driving forces and pressure factors, which corresponds to the scenario analysis and policy simulation of eco-efﬁciency implementation. Based on this analysis framework, the dynamic simulation model of an eco-efﬁciency evaluation system is divided into currency ﬂow subsystem, energy logistics subsystem and population ﬂow subsystem, and the emergy evaluation index is integrated into the energy logistics subsystem. The currency ﬂow subsystem mainly focuses on the economic operation of the Beijing-Tianjin-Hebei region, studies the input and output of the industry, and the economic growth should be in response to the society and the environment. This model of this paper will focus on the impact of labor and ﬁxed assets on the economy. The subsystem of population ﬂow provides labor supply for economic development, and the increase in human capital has a positive effect on the economy. However, the increase in Systems 2022, 10, 61 6 of 17 population will mean that more living resources are consumed and more domestic garbage is discharged, which will have a negative impact on environment quality. The energy logistics subsystem is composed of material ﬂow (resource ﬂow) and energy ﬂow. Material ﬂow records the movement state and mutual transformation process of different kinds of substances in the system, and the energy ﬂow represents the process of energy transfer and consumption in the system. The energy logistics system provides resources and power for the development of the eco-efﬁciency system. The evaluation index system of eco-efﬁciency was based on the calculation of emergy ﬂows among several subsystems. In this paper, the SD model of eco-efﬁciency evaluation subsystem (city scale) was Systems 2022, 10, x FOR PEER REVIEW 7 of 19 established on the second level by sorting out the causal relationship of inﬂuencing factors of the sub-system, in order to overcome the limitations of the “black box” of the urban eco-efﬁciency system, as shown in Figure 3. Figure 3. The second-level SD model of city scale eco-efﬁciency assessment. Figure 3. The second-level SD model of city scale eco-efficiency assessment. The Beijing Social Governance Development Report (2015–2016) showed that the population flows frequently among the three regions of Beijing, Tianjin and Hebei, and the floating population of Hebei accounts for one-fifth of Beijing’s floating population, with an increasing trend year by year. The migration of population in Beijing-Tianjin-He- bei region not only leads to the disharmony of regional development, but also affects the environmental quality. Therefore, in the first-layer model (urban agglomeration scale), we consider the flow of population factors between cities, and the evaluation system of urban agglomeration eco-efficiency is shown in Figure 4 below. Systems 2022, 10, 61 7 of 17 The Beijing Social Governance Development Report (2015–2016) showed that the population ﬂows frequently among the three regions of Beijing, Tianjin and Hebei, and the ﬂoating population of Hebei accounts for one-ﬁfth of Beijing’s ﬂoating population, with an increasing trend year by year. The migration of population in Beijing-Tianjin-Hebei region not only leads to the disharmony of regional development, but also affects the environmental quality. Therefore, in the ﬁrst-layer model (urban agglomeration scale), we Systems 2022, 10, x FOR PEER REVIEW 8 of 19 consider the ﬂow of population factors between cities, and the evaluation system of urban agglomeration eco-efﬁciency is shown in Figure 4 below. Figure 4. The first-level SD model of eco-efficiency assessment of urban agglomeration scale. Figure 4. The ﬁrst-level SD model of eco-efﬁciency assessment of urban agglomeration scale. 3.2. Model Development and Formulas 3.2. Model Development and Formulas The subsystems of the ﬁrst-layer eco-efﬁciency module are its urban components: The subsystems of the first-layer eco-efficiency module are its urban components: Beijing, Tianjin and Hebei Province. Beijing, Tianjin and Hebei Province. The second layer gives the SD model of the eco-efﬁciency evaluation of each subsystem. The second layer gives the SD model of the eco-efficiency evaluation of each subsys- Figure 3 shows the stock ﬂow diagram of the subsystem, which includes population ﬂow, tem. Figure 3 shows the stock flow diagram of the subsystem, which includes population currency ﬂow and energy ﬂow. The total population is predicted from the previous year ’s flow, currency flow and energy flow. The total population is predicted from the previous total population, births, deaths, and immigration and emigration ﬁgures. An analysis of year’s total population, births, deaths, and immigration and emigration figures. An anal- trends in previous data on births and deaths reveals small changes in birth and death rates ysis of trends in previous data on births and deaths reveals small changes in birth and in the three regions. death rates in the three regions. Po pul ation = Po pul ation + Birth + Death + I mmigration + Outmigration Population jt= Population + Birth + jt Death j+ t Immigratio n j+ t Outmigrati on jt j(t1) jt j(t− 1) jt jt jt jt This expression is a numerical equation. We use it to describe how the total population This expression is a numerical equation. We use it to describe how the total popula- is calculated. Po pul ation represents the number of people in area j in the (t 1) year. j(t1) tion is calculated. Population represents the number of people in area j in the j(t−1) The relationship between regional GDP, labor force and capital was calculated with (t − 1) year. reference to the Cobb-Douglas production function. The Cobb-Douglas production function The relationship between regional GDP, labor force and capital was calculated with is a production function created by American mathematician Cobb and economist Douglas reference to the Cobb-Douglas production function. The Cobb-Douglas production func- tion is a production function created by American mathematician Cobb and economist Douglas when they discuss the relationship between input and output. The relationship between output (GDP) and input labor (L) and capital (K) can be expressed as follows: β μ GDP = A  K  L  e The index α represents the capital elasticity, indicating that when the production capital increases by 1%, the output increases by α% on average; β is the elasticity of labor force, which means that when the labor force input into production increases by 1%, Systems 2022, 10, 61 8 of 17 when they discuss the relationship between input and output. The relationship between output (GDP) and input labor (L) and capital (K) can be expressed as follows: a b m GDP = AK L e The index a represents the capital elasticity, indicating that when the production capital increases by 1%, the output increases by a% on average; b is the elasticity of labor force, which means that when the labor force input into production increases by 1%, the output increases by b% on average; A stands for comprehensive productivity and represents technological progress, and m is the random disturbance term. Among them, Labor = total population labor coefﬁcient Increase in ﬁxed assets = investment in ﬁxed assets 0.95. Since there will inevitably be some loss and waste in the process from the beginning to the ﬁnal use of an investment, which cannot reach 100% utilization, this paper assumes that the utilization efﬁciency of ﬁxed asset investment is 95%. The depreciation method of all assets adopts the straight-line method, and the depreciation rate of ﬁxed assets was set as 9.6% [33]. K = I /P + 1 d K j,t j,t j,t j,t jt1 K , I , P , d represent ﬁxed asset stock, ﬁxed asset investment amount, ﬁxed capital j,t j,t j,t j,t investment price index and ﬁxed capital depreciation rate in year t in j region. The initial stock of ﬁxed asset comes from the existing literature [34]. The equation in this paper is set based on the existing research and the research object. The main variable equations are shown in Table 1. Table 1. Relationship of main variables. Relational Formula Serial Number Beijing Tianjin Hebei Increment of ﬁxed assets = [76.491 Increment of ﬁxed assets = [146.44 Increment of ﬁxed assets = [469.89 2 2 1 (TIME-2000) 315.35 (TIME-2000) 327.4 (TIME-2000) + 602.66] 95% (TIME-2000) + 1079.1] 95% (TIME-2000) + 2016.5] 95% Depreciation of ﬁxed assets = ﬁxed Depreciation of ﬁxed assets = ﬁxed Depreciation of ﬁxed assets = ﬁxed assets 9.6% assets 9.6% assets 9.6% lg(GDP) = 1.899 + 0.823 lgL + lg(GDP) = 1.484 + 1.054 lgL + lg(GDP) = 9.573 + 3.296 lgL + 0.813 lgK 0.565 lgK 0.433 lgK 4 Labor = population labor rate Labor = population labor rate Labor = population labor rate IF population > 23,000,000, Immigrant population = population Immigrant population = population 5 Immigrant population = population 0.003 0.002 0.002; ELSE = population 0.02 Emigration population = population Emigration population = population Emigration population = population 0.001 0.002 0.002 Wastewater emergy Wastewater emergy Wastewater emergy value = population 2.574 value = population 1.53 value = population 8.32 14 14 1 3 10 /Person + GDP 6.95 10 /Person + GDP 1.92 10 /Person + GDP 4.23 8 9 9 10 /GDP 10 /GDP 10 /GDP Emergy value of exhaust gas = GDP Emergy value of exhaust gas = GDP Emergy value of exhaust gas = GDP 6 7 7 5.19 10 1.16 10 3.03 10 Emergy input = emergy_of_foreign_direct_investment + import_emergy + international_tourism_foreign exchange_earnings_emergy 9 Emergy reduction = emergy 0.05 + emergy output 10 Energy value of waste = exhaust gas emergy value + waste water emergy value 3.3. Calculation Method of Emergy The emergy analysis method regards the research system as an energy system, takes emergy as the benchmark, and transforms the heterogeneous and non-comparable energy as well as various non-energy forms such as energy ﬂow, capital ﬂow, information ﬂow and Systems 2022, 10, 61 9 of 17 population ﬂow in the system into the same standard emergy for processing and analysis. Since all kinds of energy come from solar energy, solar energy is often used to measure a certain energy value in emergy analysis [35]. The formula is as follows: E = E . m x E represents the emergy of a material or energy; E represents the number of joules of m x material or energy available; represents the conversion of the emergy value of a material or energy, or the amount of solar energy required to produce one joule of services or products (unit: sej/J or sej/g). The urban eco-efﬁciency emergy stream was divided into local renewable emergy (R), local non-renewable emergy (N), and imported emergy from external systems (IMP). In order to minimize the risk of double counting, this paper selects the maximum renewable ﬂow (sunshine, wind, rain, river and earth cycle) to calculate the renewable resource emergy of the Beijing-Tianjin-Hebei region. The solar conversion data were taken from previous studies [24,36–38]. The emergy values of the main variables in Beijing, Tianjin and Hebei province in 2000 are shown in Table A1 of the Appendix A. 3.4. Eco-Efﬁciency Evaluation Indicators Zhang and Yang constructed an indicator to evaluate the sustainable development ability of the system from the perspective of metabolism, namely the ecological efﬁciency index (UEI) [25]. It is a function of emergy yield ratio, emergy-value ratio of non-renewable resources and contaminant emergy ratio, the higher the ecological efﬁciency index, the higher the social and economic beneﬁts of the system under unit environmental pressure (see Table 2). Therefore, this paper makes a dynamic evaluation of the eco-efﬁciency of the Beijing-Tianjin-Hebei urban agglomeration by referring to the existing research and ecological efﬁciency index (UEI) [25,39,40], as well as based on the actual situation of the Beijing-Tianjin-Hebei urban agglomeration. Table 2. Emergy evaluation index system of eco-efﬁciency. Classiﬁcation Emergy Indicators Calculation Formula Unit The energy ﬂow Updatable Resource Emergy (R) R sej/a Non-updatable resource Emergy (N) N sej/a Import Emergy (IMP) I sej/a Export Emergy (EXP) E sej/a Waste emergy value (W) W sej/a Total emergy (U) R + N + IMP sej/a The energy efﬁciency Emergy Self-sufﬁciency Ratio (ESR) (N + R)/U % Emergy Waste Ratio (EWR) W/R % Environment load ratio (ELR) (U R)/R % Emergy Yield Ratio (EYR) (R + N + IMP)/IMP % Contaminant emergy ratio (W ) W/U % Non-updatable resource emergy ratio (N ) N/U % 2 2 Eco-efﬁciency index [25] UEI EYR (1 W/U) (1 N/U) - 4. Results and Discussion Taking 2000 as the base year, the time step is one year, and the operation cycle is 2000–2035, this paper used STELLA software to simulate the high-quality development level of Beijing-Tianjin-Hebei urban agglomeration. 4.1. Model Validity Veriﬁcation Model validity analysis is a necessary step of system simulation, which can be judged by comparing the difference between simulation value and existing statistical data. The system dynamics model constructed in this paper is a concrete abstract and approximate description of the real system. Whether the model can accurately present the real system is the key to the trend prediction and policy analysis of the system. Therefore, we judged the Systems 2022, 10, 61 10 of 17 reliability of the model by comparing the difference between the simulation value and the existing statistical data [41]. The inspection period of this article is from 2015 to 2018, and the selected indicators include population, GDP, etc. Since they are the main indicators for the result analysis, and they can be calculated with a subset of historical data, the feasibility of the actual inspection is ensured. The results show that there is a certain difference in the ﬁtting degree between the simulation data and the statistical data, which is directly related to the accuracy of historical data and the logical structure of the model itself. The relevant literature indicates that when the system dynamics model is used for trend prediction, the error is acceptable within 30% [42]. Therefore, effectiveness analysis in Table 3 shows that the model can accurately describe the high-quality development status of the Beijing-Tianjin-Hebei region and has a good prediction function. Table 3. Reliability test of the eco-efﬁciency simulation system of the Beijing-Tianjin-Hebei urban agglomeration. The Real Value Simulation Value Error Year Beijing Tianjin Hebei Beijing Tianjin Hebei Beijing Tianjin Hebei Total Population (10,000) 2015–2018 2167.25 1556.50 7492.75 2133.75 1489.67 7474.74 (0.0154) (0.0429) (0.0024) GDP (billion) 2015–2018 26,918.81 17,957.16 33,330.15 25,400.10 19,758.86 33,552.27 (0.0564) 0.1003 0.0067 International tourist foreign exchange 2015–2018 508,030 353,548 56,991.75 471,500 368,461.65 70,281.46 (0.0719) 0.0422 0.2332 earnings (US $10,000) Actual utilization of foreign investment (US 2015–2018 1,691,623 1,167,307 853,895.3 1,317,312 1,000,000 1,009,490.8 (0.2213) (0.1433) 0.1822 $10,000) Total exports (US $10,000) 2015–2018 5,979,500 5,773,075 3,221,700 5,679,745 6,796,826 2,948,552 (0.0501) 0.1773 (0.0848) Total import (US $10,000) 2015–2018 27,473,500 7,456,900 1,823,300 34,969,494 8,093,880 2,268,320 0.2728 0.0854 0.2441 4.2. Analysis of Simulation Results 4.2.1. Eco-Efﬁciency of Beijing-Tianjin-Hebei Urban Agglomeration The eco-efﬁciency index (UEI) is a sustainable development index that reﬂects urban resource efﬁciency, environmental efﬁciency and economic efﬁciency. As can be seen from Figure 5, although the Beijing-Tianjin-Hebei region advocates green production, the overall eco-efﬁciency index was not high, with an average of 0.3786 from 2000 to 2035. During the simulation period, the eco-efﬁciency of the Beijing-Tianjin-Hebei urban agglomeration showed a trend of increasing ﬁrst and then decreasing, reaching a maximum value in 2011. This is similar to the conclusion of Ren and Fang on the county-scale evaluation of eco-efﬁciency in the Beijing-Tianjin-Hebei urban agglomeration; that is, that the overall level of eco-efﬁciency is low, and most eco-efﬁciency values are below 0.4 [43]. This may be due to the fact that in the early stage of the development of the Beijing-Tianjin urban agglomeration, there were relatively few residents and resource-intensive industries, resulting in less waste discharge. With the continuous expansion of urbanization, the migration of residents and enterprises leads to the massive consumption of renewable resources such as hydropower, wind energy and geothermal energy, as well as the increase in waste emissions. Therefore, UEI shows a trend of rising ﬁrst and then falling. Next, we further analyzed the changes of the eco-efﬁciency index under different situations. As shown in Figure 6, when the actual utilization of foreign investment, interna- tional tourism foreign exchange earnings and import value of Beijing, Tianjin, and Hebei increase by 10%, it is conducive to the improvement of eco-efﬁciency. On the contrary, a 10% decrease in birth rate and ﬁxed asset investment of Beijing Tianjin and Hebei con- tributed to the increase in eco-efﬁciency. Previous studies have also shown that population agglomeration in the Beijing-Tianjin-Hebei region has a signiﬁcant negative impact on eco-efﬁciency [44]. Therefore, the Beijing-Tianjin-Hebei urban agglomeration must pay attention to the synchronous improvement of weight and quality in the process of intro- ducing actual utilization of foreign capital. In addition, tourism is a “smoke-free industry”, Systems 2022, 10, 61 11 of 17 and the foreign exchange income from tourism is compatible with the development of green industries, so it should be vigorously advocated. The data show that, in 2019, the international tourism foreign exchange income of Beijing, Tianjin and Hebei Province accounted for about 5.42% of the national international tourism foreign exchange income. At the same time, in the process of coordinated development of Beijing-Tianjin-Hebei, how Systems 2022, 10, x FOR PEER REVIE to Wadjust the population scale and reasonably arrange the ﬁxed asset investment to 12 achieve of 19 the improvement of eco-efﬁciency is a problem that needs to be discussed. Figure 5. Eco-efficiency index of Beijing-Tianjin-Hebei urban agglomeration from 2000 to 2035. Systems 2022, 10, x FOR PEER REVIEW 13 of 19 Figure 5. Eco-efﬁciency index of Beijing-Tianjin-Hebei urban agglomeration from 2000 to 2035. Next, we further analyzed the changes of the eco-efficiency index under different 0.40 situations. As shown in Figure 6, when the actual utilization of foreign investment, inter- national tour0. is 39 m foreign exchange earnings and import value of Beijing, Tianjin, and He- bei increase by 10%, it is conducive to the improvement of eco-efficiency. On the contrary, 0.38 a 10% decrease in birth rate and fixed asset investment of Beijing Tianjin and Hebei con- 0.37 tributed to the increase in eco-efficiency. Previous studies have also shown that popula- tion agglomeration in the Beijing-Tianjin-Hebei region has a significant negative impact 0.36 on eco-efficiency [44]. Therefore, the Beijing-Tianjin-Hebei urban agglomeration must pay 0.35 attention to the synchronous improvement of weight and quality in the process of intro- ducing actual utilization of foreign capital. In addition, tourism is a “smoke-free indus- 0.34 Internationa Actual try”, and the foreign exchange income from tourism is compatible with the development l tourist Original Fixed asset utilization Import Birth rate foreign of green industries, so it should be vigorously advocated. The data show that, in 2019, the value investment of foreign value exchange investment international tourism foreign exchange income of Beijing, Tianjin and Hebei Province ac- earnings counted for about 5.42% of the national international tourism foreign exchange income. key element+10% 0.3786 0.3777 0.3781 0.3804 0.3792 0.3968 key element−10% 0.3786 0.3795 0.3791 0.3767 0.3779 0.3592 At the same time, in the process of coordinated development of Beijing-Tianjin-Hebei, how to adjust the population scale and reasonably arrange the fixed asset investment to Figure 6. Changes of average eco-efficiency index from 2000 to 2035 under different simulation Figure 6. Changes of average eco-efficiency index from 2000 to 2035 under different simulation scenarios. scenarios. Note: The horizontal axis represents the parameters adjusted in different simulation achieve the improvement of eco-efficiency is a problem that needs to be discussed. scenarios. Note: The horizontal axis represents the parameters adjusted in different simulation scenarios. 4.2.2. Analysis of Eco-Efficiency Indicators of Beijing-Tianjin-Hebei Urban Agglomera- 4.2.2. Analysis of Eco-Efﬁciency Indicators of Beijing-Tianjin-Hebei Urban Agglomeration tion (1) Emergy waste ratio (EWR) (1) Emergy waste ratio (EWR) The emergy waste rate (EWR) is the ratio of waste emergy to renewable resource The emergy waste rate (EWR) is the ratio of waste emergy to renewable resource emergy, which is used to evaluate the availability of waste discharged by the system and emergy, whichth is e rused ecycling to cap evaluate acity of the the system availability . As shown in of Figwaste ure 7, thedischar waste ratged e of em by ergthe y is system and increasing from 2000 to 2035. The simulation results of emergy waste rate under different the recycling capacity of the system. As shown in Figure 7, the waste rate of emergy is scenarios show that when the birth rate and fixed asset investment in Beijing, Tianjin and increasing from 2000 to 2035. The simulation results of emergy waste rate under different Hebei province increase by 10%, the emergy waste rate of Beijing-Tianjin-Hebei urban agglomeration increases. scenarios show that when the birth rate and ﬁxed asset investment in Beijing, Tianjin and Hebei province increase by 10%, the emergy waste rate of Beijing-Tianjin-Hebei urban Original value 4.5 agglomeration increases. (Birth rate)+10% 3.5 (Birth rate)−10% (Fixed asset investment)+10% 2.5 (Fixed asset investment)−10% (Actual utilization of foreign investment)+10% 1.5 (Actual utilization of foreign investment)−10% (International tourist foreign exchange earnings)+10% 0.5 (International tourist foreign 0 exchange earnings)−10% (Import value)+10% (Import value)−10% time Emergy Waste Ratio Ecological efficiency index of Beijing- Tianjin-Hebei urban agglomeration Systems 2022, 10, x FOR PEER REVIEW 13 of 19 0.40 0.39 0.38 0.37 0.36 0.35 0.34 Internationa Actual l tourist Original Fixed asset utilization Import Birth rate foreign value investment of foreign value exchange investment earnings key element+10% 0.3786 0.3777 0.3781 0.3804 0.3792 0.3968 key element−10% 0.3786 0.3795 0.3791 0.3767 0.3779 0.3592 Figure 6. Changes of average eco-efficiency index from 2000 to 2035 under different simulation scenarios. Note: The horizontal axis represents the parameters adjusted in different simulation scenarios. 4.2.2. Analysis of Eco-Efficiency Indicators of Beijing-Tianjin-Hebei Urban Agglomera- tion (1) Emergy waste ratio (EWR) The emergy waste rate (EWR) is the ratio of waste emergy to renewable resource emergy, which is used to evaluate the availability of waste discharged by the system and the recycling capacity of the system. As shown in Figure 7, the waste rate of emergy is increasing from 2000 to 2035. The simulation results of emergy waste rate under different scenarios show that when the birth rate and fixed asset investment in Beijing, Tianjin and Systems 2022, 10, 61 12 of 17 Hebei province increase by 10%, the emergy waste rate of Beijing-Tianjin-Hebei urban agglomeration increases. Original value 4.5 (Birth rate)+10% 3.5 (Birth rate)−10% (Fixed asset investment)+10% 2.5 (Fixed asset investment)−10% (Actual utilization of foreign investment)+10% 1.5 (Actual utilization of foreign investment)−10% (International tourist foreign exchange earnings)+10% 0.5 (International tourist foreign 0 exchange earnings)−10% (Import value)+10% (Import value)−10% time Figure 7. Simulation results of emergy waste rate of the Beijing-Tianjin-Hebei urban agglomeration. (2) Contaminant emergy ratio The contaminant emergy ratio is the ratio of the sum emergy of the “three wastes” to the total emergy, which is used to measure the burden of waste on the entire system. The larger the contaminant emergy ratio is, the larger the amount of waste discharged from the system is, and the greater the degree of utilization of waste from the system is [45]. It can be seen from Figure 8 that the contaminant emergy ratio in the Beijing-Tianjin-Hebei region decreased ﬁrst and then increased during the simulation period. In the case of original value, the contaminant emergy ratio in 2011 and 2035 are 0.0097 and 0.026, respectively. The simulation results of the waste emergy ratio under different scenarios show that the contaminant emergy ratio increases when the birth rate and fixed asset investment increase by 10%, and the actual utilization of foreign investment, tourism foreign exchange income, and imports decrease by 10%. The sustainability of economic development is affected by the recycling rate of waste. Therefore, there are still some urgent tasks for environmental regulation, such as energy conservation under the guidance of urban transformation. (3) Emergy yield ratio (EYR) Emergy yield ratio (EYR) is an indicator that measures the contribution of system output to economic development. The higher the EYR, the higher the emergy return rate of the system. It also means under the same economic input, the higher emergy output will be obtained, that is, the higher the production efﬁciency of the system. As shown in Figure 9, the emergy yield ratio of the Beijing-Tianjin-Hebei urban agglomeration ﬂuctuated between 1.5 and 5.5 from 2000 to 2035, and has been on the rise since 2011, indicating that the economic efﬁciency of energy and resource utilization of Beijing-Tianjin-Hebei urban agglomeration has been improved recently. When the actual utilization of foreign capital, the foreign exchange income of international tourism and the import volume decreased by 10%, the emergy yield ratio increased. Emergy Waste Ratio Ecological efficiency index of Beijing- Tianjin-Hebei urban agglomeration Systems 2022, 10, x FOR PEER REVIEW 14 of 19 Figure 7. Simulation results of emergy waste rate of the Beijing-Tianjin-Hebei urban agglomera- tion. (2) Contaminant emergy ratio The contaminant emergy ratio is the ratio of the sum emergy of the “three wastes” to the total emergy, which is used to measure the burden of waste on the entire system. The larger the contaminant emergy ratio is, the larger the amount of waste discharged from the system is, and the greater the degree of utilization of waste from the system is [45]. It can be seen from Figure 8 that the contaminant emergy ratio in the Beijing-Tianjin-Hebei region decreased first and then increased during the simulation period. In the case of orig- inal value, the contaminant emergy ratio in 2011 and 2035 are 0.0097 and 0.026, respec- tively. The simulation results of the waste emergy ratio under different scenarios show that the contaminant emergy ratio increases when the birth rate and fixed asset investment increase by 10%, and the actual utilization of foreign investment, tourism foreign ex- change income, and imports decrease by 10%. The sustainability of economic develop- ment is affected by the recycling rate of waste. Therefore, there are still some urgent tasks Systems 2022, 10, 61 13 of 17 for environmental regulation, such as energy conservation under the guidance of urban transformation. 0.0325 Original value (Birth rate)+10% 0.0275 (Birth rate)−10% (Fixed asset investment)+10% 0.0225 (Fixed asset investment)−10% (Actual utilization of foreign 0.0175 investment)+10% (Actual utilization of foreign investment)−10% 0.0125 (International tourist foreign exchange earnings)+10% (International tourist foreign exchange 0.0075 earnings)−10% (Import value)+10% (Import value)−10% time Figure 8. Simulation results of contaminant emergy ratio of the Beijing-Tianjin-Hebei urban ag- Systems 2022, 10, x FOR PEER REVIEW 15 of 19 Figure 8. Simulation results of contaminant emergy ratio of the Beijing-Tianjin-Hebei urban glomeration. agglomeration. (3) Emergy yield ratio (EYR) Emergy yield ratio (EYR) is an indicator that measures the contribution of system 5.5 Original value output to economic development. The higher the EYR, the higher the emergy return rate of the system. It also means under the same economi (Birc th i n ra p te u )t +10% , the higher emergy output will be obtained, that is, the higher the production efficiency of the system. As shown in 4.5 (Birth rate)−10% Figure 9, the emergy yield ratio of the Beijing-Tianjin-Hebei urban agglomeration fluctu- ated between 1.5 and 5.5 from 2000 to 2035, and has been on the rise since 2011, indicating (Fixed asset investment)+10% that the economic efficiency of energy and resource utilization of Beijing-Tianjin-Hebei (Fixed asset investment)−10% urban agglomeration has been improved recently. When the actual utilization of foreign 3.5 capital, the foreign exchange income of international tourism and the import volume de- (Actual utilization of foreign creased by 10%, the emergy yield ratio increased. investment)+10% (Actual utilization of foreign 2.5 investment)−10% (International tourist foreign 2 exchange earnings)+10% (International tourist foreign exchange earnings)−10% 1.5 (Import value)+10% (Import value)−10% time Figure 9. Simulation results of emergy yield ratio of the Beijing-Tianjin-Hebei urban agglomera- Figure 9. Simulation results of emergy yield ratio of the Beijing-Tianjin-Hebei urban agglomeration. tion. (4) Environmental load ratio (ELR) (4) Environmental load ratio (ELR) Environmental load ratio (ELR) is the ratio of purchased and non-renewable local emergy Environmental load ratio (ELR) is the ratio of purchased and non-renewable local to free environmental emergy (renewable resource emergy). The environmental load rate emergy to free environmental emergy (renewable resource emergy). The environmental represents the pressure on the environment caused by the economic activities of the system [46]. load rate represents the pressure on the environment caused by the economic activities of When ELR < 3, the system environment bears less pressure and belongs to a healthy state; the system [46]. When ELR < 3, the system environment bears less pressure and belongs when 3 < ELR < 10, the system environment pressure is at a medium level and belongs to to a healthy state; when 3 < ELR < 10, the system environment pressure is at a medium sub-health state, and when ELR > 10, the system environment pressure is too high, which is level and belongs to sub-health state, and when ELR > 10, the system environment pres- an unhealthy state [25]. As shown in Figure 10, the environmental load rate of the Beijing- sure is too high, which is an unhealthy state [25]. As shown in Figure 10, the environmen- tal load rate of the Beijing-Tianjin-Hebei urban agglomeration showed an upward trend from 2000 to 2035, indicating that the pressure on the environment caused by system eco- nomic activities continued to increase. In the case of the original value, the average value of the environmental load rate from 2000 to 2035 is 94.57, which belongs to an unhealthy state. It shows that the pressure of urban ecosystem economic activities on the environ- ment in the Beijing-Tianjin-Hebei urban agglomeration is too large and does not weaken with the development of the city. The simulation results show that the environmental load rate is greatly affected by the amount of foreign capital, foreign exchange income from international tourism and imports, which is determined by the connotation of the environmental load rate. From the perspective of emergy analysis, a large number of emergy inputs from the outside and over-exploitation of local non-renewable resources are the main reasons of high environ- mental load rate. Contaminant emergy ratio Emergy Yield Ratio Systems 2022, 10, 61 14 of 17 Tianjin-Hebei urban agglomeration showed an upward trend from 2000 to 2035, indicating that the pressure on the environment caused by system economic activities continued to increase. In the case of the original value, the average value of the environmental load rate from 2000 to 2035 is 94.57, which belongs to an unhealthy state. It shows that the pressure of Systems 2022, 10, x FOR PEER REVIEW 16 of 19 urban ecosystem economic activities on the environment in the Beijing-Tianjin-Hebei urban agglomeration is too large and does not weaken with the development of the city. Original value (Birth rate)+10% (Birth rate)−10% (Fixed asset investment)+10% (Fixed asset investment)−10% (Actual utilization of foreign investment)+10% (Actual utilization of foreign investment)−10% (International tourist foreign exchange earnings)+10% (International tourist foreign exchange earnings)−10% （Import value)+10% （Import value)−10% time Figure 10. Simulation results of environmental load ratio of the Beijing-Tianjin-Hebei urban ag- Figure 10. Simulation results of environmental load ratio of the Beijing-Tianjin-Hebei urban agglom- glomeration. eration. 5. Conclusion and Suggestion The simulation results show that the environmental load rate is greatly affected by the In this paper, the emergy analysis and system dynamics method are combined to amount of foreign capital, foreign exchange income from international tourism and imports, establish the eco-economic system dynamics model of Beijing-Tianjin-Hebei urban ag- whg ic lh ome is d rat etie on rm biy n e ud sin bg y S th tel e lc ao s n of nto wa tatrie, on an od f tth he e e dn ev viel ro op nm me en ntt a slta lo tu as d an rad te m . F ot ro iv m atiton he of pe trh se p ective of e sm yse tem rgy ar an e a an lyal siy sze , adl a th rg rou e n gu hm sib mu erlo atfion em . e Tr h g ey resu inplu tst s sh fr ow om th th ate : (1) ou t F srio dm e a 2n 000 d o to v e 2035 r-ex,p th lo e itation eco-efficiency of the Beijing-Tianjin-Hebei urban agglomeration was not high, showing a of local non-renewable resources are the main reasons of high environmental load rate. trend of first rising and then falling. Compared with the value of eco-efficiency index in 5. Conclusions 2000, it increase and d bSuggestion y 13.28% in 2035. The analysis under different situations shows that the synchronous improvement of the quantity and quality of foreign capital actually uti- In this paper, the emergy analysis and system dynamics method are combined to lized, as well as the adjustment of population scale and rational arrangement of fixed as- establish the eco-economic system dynamics model of Beijing-Tianjin-Hebei urban ag- sets investment are conductive to the improvement of eco-efficiency; (2) The analysis of glomeration by using Stella software, and the development status and motivation of the various indicators of eco-efficiency of the Beijing-Tianjin-Hebei urban agglomeration system are analyzed through simulation. The results show that: (1) From 2000 to 2035, the shows that the emergy waste rate is rising, the environmental load rate is in an unhealthy eco-efﬁciency of the Beijing-Tianjin-Hebei urban agglomeration was not high, showing a state, and the decline of system emergy output efficiency because of the environmental trend of ﬁrst rising and then falling. Compared with the value of eco-efﬁciency index in pressure on the growth of imported emergy and non-renewable resource emergy is rising. 2000, it increased by 13.28% in 2035. The analysis under different situations shows that the Therefore, high environmental pressure, low re-use rate of pollutants and low production synchronous improvement of the quantity and quality of foreign capital actually utilized, efficiency of the system are important reasons for low eco-efficiency in regional economic development. According to the emergy analysis theory, if the Beijing-Tianjin-Hebei urban as well as the adjustment of population scale and rational arrangement of ﬁxed assets agglomeration wants to truly realize the high-quality development of economy, some fea- investment are conductive to the improvement of eco-efﬁciency; (2) The analysis of various sible approaches are to improve the utilization rate of renewable resources in the region, indicators of eco-efﬁciency of the Beijing-Tianjin-Hebei urban agglomeration shows that appropriately limit the input of external feedback emergy, and at the same time establish the emergy waste rate is rising, the environmental load rate is in an unhealthy state, and a recycling mechanism of waste resources and energy to improve the social and economic the decline of system emergy output efﬁciency because of the environmental pressure on benefits exchanged by unit resources, energy and environment. the growth of imported emergy and non-renewable resource emergy is rising. Therefore, This paper combines emergy analysis with the system dynamics method to show the high environmental pressure, low re-use rate of pollutants and low production efﬁciency of relationship between the system structure and factors through the system dynamics the system are important reasons for low eco-efﬁciency in regional economic development. model, and uses simulation technology to grasp the future high-quality development of According to the emergy analysis theory, if the Beijing-Tianjin-Hebei urban agglomeration urban agglomerations. In future work, more details can be considered in the model devel- wants to truly realize the high-quality development of economy, some feasible approaches opment to reduce the impact of data limitations and increase the integrity and authenticity areof to thimpr e systove em sthe imulutilization ation. rate of renewable resources in the region, appropriately Environmental load ratio Systems 2022, 10, 61 15 of 17 limit the input of external feedback emergy, and at the same time establish a recycling mechanism of waste resources and energy to improve the social and economic beneﬁts exchanged by unit resources, energy and environment. This paper combines emergy analysis with the system dynamics method to show the relationship between the system structure and factors through the system dynamics model, and uses simulation technology to grasp the future high-quality development of urban agglomerations. In future work, more details can be considered in the model development to reduce the impact of data limitations and increase the integrity and authenticity of the system simulation. Author Contributions: All authors contributed equivalently to this research. H.L. developed the original idea. All authors designed this study, collected, and analyzed the data. H.H. established the model and wrote the ﬁrst paper. X.B. provided advice on data collection, as well as reviewed and edited the manuscript. W.C. reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: The data presented in this study are available on request from the corresponding author. Conﬂicts of Interest: The authors declare no conﬂict of interest. Appendix A In this paper, the conversion rate of energy is mainly based on existing research [24,36–38], and the global energy reference line of 9.26 10 sej/year is used as the standard for conver- sion. The 2000 energy value analysis table of the Beijing-Tianjin-Hebei urban agglomeration is calculated, as shown in Table A1 below. Table A1. Main energy ﬂow analysis table of Beijing-Tianjin-Hebei Eco-economic system in 2000. Energy Solar Items Initial Data Conversion Energy Reference Rate Value Beijing Tianjin Hebei Beijing Tianjin Hebei 19 1 6 21 1 9 1 6 21 The solar energy 9.33 10 7.12 10 1.06 10 1.00 9.33 10 7.12 10 1.06 10 [24] 1 7 1 6 18 2 1 9 1 9 20 wind energy 1.28 10 9.77 10 1.46 10 6.32 10 8.09 10 6.17 10 9.22 10 [24] Chemical energy of 1 6 1 6 1 7 4 20 20 22 4.10 10 3.16 10 9.38 10 1.82 10 7.46 10 5.75 10 1.71 10 [24] Updatable rainwater resource energy The rain potential 15 14 1 6 3 1 9 18 20 2.54 10 2.07 10 7.58 10 8.89 10 2.26 10 1.84 10 6.74 10 [24] values (R) energy Earth rotation 1 6 1 6 1 7 4 20 20 21 1.65 10 1.26 10 1.88 10 2.90 10 4.79 10 3.65 10 5.45 10 [36] energy 21 21 22 subtotal 1.42 10 1.00 10 2.52 10 1 6 1 6 1 7 4 20 20 22 Surface soil loss 1.05 10 1.35 10 2.19 10 7.40 10 7.77 10 9.99 10 1.62 10 [24] 1 7 1 7 18 4 22 22 23 The raw coal (J) [24] 5.68 10 5.17 10 2.53 10 4.00 10 2.27 10 2.07 10 1.01 10 1 7 1 7 1 7 4 22 22 22 Crude oil (J) 3.15 10 2.97 10 3.12 10 5.40 10 1.70 10 1.6 10 1.69 10 [24] Non-updatable 6 6 7 15 22 21 22 cement (t) 8.27 10 2.68 10 4.69 10 2.07 10 1.71 10 5.55 10 9.71 10 [37] resource energy 16 16 16 4 21 21 21 Natural gas (J) 4.24 10 2.10 10 3.10 10 4.80 10 2.04 10 1.01 10 1.49 10 [38] values 6 6 7 1 5 21 21 22 steel (t) 6.97 10 3.16 10 1.31 10 1.40 10 9.76 10 4.42 10 1.83 10 [24] 1 7 16 1 7 5 22 22 22 Thermal power [36] 1.38 10 8.42 10 2.91 10 1.60 10 2.21 10 1.35 10 4.66 10 22 22 23 subtotal 9.15 10 6.22 10 2.98 10 1 0 9 9 12 22 22 21 imports 3.74 10 8.53 10 1.53 10 2.50 10 9.36 10 2.13 10 3.82 10 [38] International tourist 9 8 8 12 21 20 20 foreign exchange 2.77 10 1.42 10 2.32 10 2.50 10 6.93 10 3.55 10 5.80 10 [38] Import Emergy earnings Actual utilization of 20 21 21 21 3.01 10 2.56 10 1.39 10 2.50 10 7.53 10 6.40 10 3.48 10 [38] foreign investment 23 22 21 subtotal 1.08 10 2.81 10 7.88 10 1 0 9 9 12 22 22 21 Export Emergy exports 1.20 10 8.63 10 3.71 10 1.46 10 1.75 10 1.26 10 5.42 10 [38] 1 3 1 3 1 4 4 1 8 1 8 1 9 Waste gas 7.74 10 4.20 10 2.37 10 4.80 10 3.72 10 2.01 10 1.14 10 [36] Waste energy 1 5 1 5 1 5 5 21 21 21 Waste water [24] 4.47 10 2.04 10 7.40 10 8.60 10 3.85 10 1.75 10 6.36 10 value 21 21 21 subtotal 3.85 10 1.76 10 6.37 10 Systems 2022, 10, 61 16 of 17 References 1. 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Abstract

systems Article Eco-Efﬁciency Assessment of Beijing-Tianjin-Hebei Urban Agglomeration Based on Emergy Analysis and Two-Layer System Dynamics 1 1 , 2 1 Huanhuan Huo , Haiyan Liu *, Xinzhong Bao and Wei Cui School of Economics and Management, China University of Geosciences (Beijing), Beijing 100083, China; huohh@cugb.edu.cn (H.H.); cuiw@cugb.edu.cn (W.C.) Management College, Beijing Union University, Beijing 100101, China; baoxz@buu.edu.cn * Correspondence: liuhy@cugb.edu.cn; Tel.: +86-010-82321343 Abstract: In the process of the economic development of the Beijing-Tianjin-Hebei urban agglom- eration, ecological and environmental issues are still an important factor restricting high-quality development. The study of eco-efﬁciency is of great signiﬁcance for coordinating the relationship between economy, resources and environment. This paper used a combinated method of two-layer system dynamics and emergy analysis to construct an emergy–system dynamics coupling model for eco-efﬁciency evaluation from the subsystems of resource ﬂow, energy ﬂow, currency ﬂow and population ﬂow of urban system, which is used to simulate and analyze the eco-efﬁciency of the Beijing-Tianjin-Hebei urban agglomeration. The results show that the overall eco-efﬁciency of the Beijing-Tianjin-Hebei urban agglomeration is not high, with an average value of 0.3786, and there is a trend of the value rising ﬁrst and then falling from 2000 to 2035. The index values of emergy waste rate, contaminant emergy ratio, emergy output rate and environmental load rate after the Citation: Huo, H.; Liu, H.; Bao, X.; decomposition of the eco-efﬁciency show that the high environmental pressure, the low re-use rate Cui, W. Eco-Efﬁciency Assessment of of pollutants and the low production efﬁciency of the system are important reasons for the low Beijing-Tianjin-Hebei Urban eco-efﬁciency in regional economic development. Finally, through scenario simulation, we propose Agglomeration Based on Emergy Analysis and Two-Layer System that optimizing the economic structure, adjusting the population size and rationally arranging the Dynamics. Systems 2022, 10, 61. ﬁxed assets investment are conducive to improving the eco-efﬁciency of the Beijing-Tianjin-Hebei https://doi.org/10.3390/ urban agglomeration. systems10030061 Keywords: eco-efﬁciency; two-layer system dynamics; emergy analysis; Beijing-Tianjin-Hebei ur- Academic Editors: Jinan Fiaidhi, ban agglomeration Aboul Ella Hassanien and Hye-jin Kim Received: 28 March 2022 Accepted: 30 April 2022 1. Introduction Published: 8 May 2022 Under the guidance of the national integration policy and the promotion of the urban Publisher’s Note: MDPI stays neutral upgrading, urban agglomerations are gradually becoming a new carrier of economic de- with regard to jurisdictional claims in velopment. High-quality development of urban agglomerations can optimize the regional published maps and institutional afﬁl- development pattern and drive the high-quality development of the whole economy [1]. iations. The Beijing-Tianjin-Hebei urban agglomeration is an important part of China’s core area, the outline of Beijing-Tianjin-Hebei Coordinated Development Planning issued in 2015 clearly stated that “by 2035, the structure and regional integration pattern of the Beijing-Tianjin- Hebei world-class urban agglomeration will be basically formed, the regional economic Copyright: © 2022 by the authors. structure will be more reasonable, and the quality of the ecological environment will be Licensee MDPI, Basel, Switzerland. generally well”. In 2021, The 14th Five-Year Plan for the National Economic and Social This article is an open access article Development of the People’s Republic of China and the Outline of the Vision for 2035 even distributed under the terms and put “accelerating the coordinated development of Beijing, Tianjin and Hebei” at the top conditions of the Creative Commons of the “in-depth implementation of major regional strategies”, and listed it as the “ﬁrst Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ echelon of accelerating high-quality development” nationwide. High-quality development 4.0/). means that the work of ecological and environmental protection cannot be ignored in the Systems 2022, 10, 61. https://doi.org/10.3390/systems10030061 https://www.mdpi.com/journal/systems Systems 2022, 10, 61 2 of 17 process of economic development. Therefore, quantifying and coordinating the relationship between economic development and resources and the environment of the Beijing-Tianjin- Hebei urban agglomeration is of great signiﬁcance in order to promote the high-quality development of the Beijing-Tianjin-Hebei region. The research on the economic development and ecological environment of the Beijing- Tianjin-Hebei region focuses on the impact of economic development on the ecological environment. For example, He and Cai measure and analysis the degree of decoupling degree between economic growth and environmental resources in the Beijing-Tianjin-Hebei region, and ﬁnd that the rapid economic development in the Beijing-Tianjin-Hebei region has not fully realize the negative growth of resource consumption, as well as resource utilization rate and economical utilization rate are still at a low level [2]. Wang ﬁnd that the coupling and coordinated development between economic society and ecolog- ical environment of the Beijing-Tianjin-Hebei urban agglomeration presented dynamic evolution (represented as S-shaped), showing an overall upward trend, and the growth mode gradually changed from the economic growth lag to the ecological environment lag [3]. Zhang et al. use the panel data approach (PDA) to examine the causal impact of the Beijing-Tianjin-Hebei strategy on Hebei’s economy and environment under a counterfac- tual framework. The main ﬁnding is that the Beijing-Tianjin-Hebei strategy signiﬁcantly increases the proportion of Hebei’s tertiary industry in GDP and signiﬁcantly reduces the geographic average PM2.5 concentration, but it has no signiﬁcant impact on Hebei’s GDP growth rate [4]. Xue and Zhou use the DDF-GML index to measure the green total factor productivity in the Beijing-Tianjin-Hebei region from 2005 to 2018, and found that there were “low growth” and “unbalanced” problems in the green total factor productivity during the sample period [5]. To sum up, we can see that the environmental quality of the Beijing-Tianjin-Hebei region has been improved in the process of economic development, but low energy utilization efﬁciency and environmental problems are still important factors restricting high-quality development. Eco-efﬁciency as a comprehensive index reﬂecting the situation of economic, resource and environmental [6], the evaluation of urban eco-efﬁciency can objectively evaluate the efﬁciency relationship between the overall resource allocation, environmental quality and economic development of a city, so as to guide the coordinated and sustainable development of cities [7]. Some scholars adopted the single ratio method [8], the emergy value (or material ﬂow) account accounting method [9], the index system method [10] and the model method (including data envelopment analysis (DEA) and stochastic frontier analysis method (SFA)) [11,12] to explore the level and spatial differences of eco-efﬁciency in cities and urban agglomerations. Due to the advantages of using fewer indexes and the fact that it can directly process the indexes of different dimensions, DEA is widely used in efﬁciency evaluation in various ﬁelds. For example, Gai and Zhan use the SBM model that considers the undesired output to measure the marine eco-efﬁciency of China’s coastal provinces, and they describe the evolution characteristics of the spatial pattern with the help of the center of gravity model [13]. Tu et al. use super-efﬁciency (SBM) and Malmquist index to measure the eco-efﬁciency of the Pearl River Delta urban agglomeration from both static and dynamic aspects [14]. Zhang et al. use the Super-SBM model with unexpected outputs and standard deviation ellipses to study the dynamic changes and spatiotemporal differences of urban eco-efﬁciency in the lower Yellow River [15]. However, the calculation of eco-efﬁciency based on DEA model regards the region as a “black box”, which cannot reﬂect the internal structure of the regional eco-economic system, and does not take into account the interaction between its internal subsystems. With the development of social economy and the improvement of management prac- tice ability, it is required to open the efﬁciency evaluation “black box” and deeply under- stand the interior of the decision-making unit. System dynamics (SD) [16] emphasizes the consideration of the problem as a hole, and understands the composition of the problem and the interaction between various parts, as well as using dynamic simulation to investi- gate the dynamic change behavior and development trend of the system [17]. Its essence is Systems 2022, 10, 61 3 of 17 to open the “black box” of the decision-making unit, decompose the complex system, and investigate the inﬂuence of each link on the overall efﬁciency of the system. Currently, it has been applied to study the interaction relation between environmental and economic factors [18], sustainable development strategy research of highway systems [19], and the evaluation of regional circular economy [20], and so on. However, with the strengthening of the ﬂowing role of economic, resource and other factors in the global and regional urban networks, isolated point-like cities gradually evolve into closely interconnected planar urban agglomerations [21]. Urban agglomerations are interconnected by the elements of population, resources and economy within the urban agglomeration. Therefore, based on the existing studies [22,23], this paper introduces two-layer system dynamics (the ﬁrst layer is the spatial layout of the urban agglomeration, namely the Beijing, Tianjin and Hebei province, and the second layer is the relationship of urban internal factors) to evaluate the eco-efﬁciency of the Beijing-Tianjin-Hebei urban agglomeration. The eco-efﬁciency evaluation system based on system dynamics is a complex system integrating economic, social and environmental factors. These factors interact with each other, but it is difﬁcult to conduct a comprehensive and consistent analysis of the interaction between them due to the different equivalents. Emergy analysis (EMA) was ﬁrst proposed by Odum in the 1980s [24]. It can convert all types of resources (whether energy or matter) into one form of energy, namely solar energy, that makes it possible to study various types of materials, energy and capital in one system [25]. In addition, emergy-based indicators such as emergy output rate, emergy load rate, and eco-efﬁciency index are directly linked to urban ecosystems in an integrated way by incorporating service value [26], that can reﬂect environmental pressure, eco-efﬁciency, changes in energy structure, and resource utilization, etc. Therefore, this method has been widely applied to the sustainability evaluation of urban circular economy [27], industrial ecosystems [9] and regional economic systems [28]. In this paper we combine it with system dynamics to make up for the deﬁciency of different equivalent of system dynamics. Based on the perspective of functional ﬂow, this paper takes the Beijing-Tianjin-Hebei urban agglomeration as the research object, constructing an emergy-SD coupling model for eco-efﬁciency evaluation by the method of emergy analysis and the system dynamics. Finally, the eco-efﬁciency of the Beijing-Tianjin-Hebei urban agglomeration is evaluated, and the trend of eco-efﬁciency under different scenarios is discussed, which provides scientiﬁc reference for the effective implementation of urban development strategy. Next, we introduce the research ﬁelds and data sources. Then, the framework of urban agglomeration eco-efﬁciency evaluation is given and the developed model and its formula, calculation method, parameters and evaluation index are explained. Finally, we will verify the model and analyze the simulation results. 2. Research Objects and Data Sources The Beijing-Tianjin-Hebei urban agglomeration takes the capital Beijing and municipality Tianjin as the center, and other major cities include Shijiazhuang, Baoding, Langfang, Handan, etc. (as shown in Figure 1). Statistics in 2020 show that in the Beijing-Tianjin-Hebei region, Beijing hosts 20.37% of the resident population on 7.6% of the land, creating 41.78% of the regional output value; 5.5% of the land in Tianjin hosts 12.90% of the resident population and creates 16.32% of the regional output value. Of the land in in the Hebei province, 86.9% bears 66.73% of the permanent population and creates 41.89% of the regional output value. At the beginning of this century, scholar Wu Liangyong proposed the Greater Beijing Plan, which is usually regarded as the beginning of the integration of Beijing, Tianjin and Hebei. After that, the coordinated development of Beijing, Tianjin and Hebei has experienced three stages, namely, the three regions reached consensus on the cooperation of the Beijing-Tianjin-Hebei region, the initial formulation of regional development planning, and the coordinated development of the Beijing-Tianjin-Hebei region was elevated to a national strategy level and its implementation was accelerated. Recently, Beijing issued the “Implementation Plan on Establishing a More Effective New Mechanism for Coordinated Systems 2022, 10, 61 4 of 17 Regional Development”, which proposed that by 2035, the framework of Beijing-Tianjin- Hebei world-class urban agglomeration will be basically formed. Therefore, this paper takes 2000–2035 as the research period. The following basic data were used: the total population of Beijing, Tianjin and Hebei Province are 1382, 1001.14 and 6674 million, respectively. The emergy data of Beijing-Tianjin-Hebei resource stock are calculated by emergy analysis. Systems 2022, 10, x FOR PEER REVIEW 4 of 19 Updatable resources include solar energy, wind energy, rainwater chemical energy and potential energy, which provides driving forces for the ecological economic system. Unable to update resources include oil, natural gas, etc. At the same time, the area also imports goods and equipment from the outside, and exports sewage and garbage to the outside. Figure 1. Beijing-Tianjin-Hebei urban agglomeration. Figure 1. Beijing-Tianjin-Hebei urban agglomeration. The original data used in this study came from China Statistical Yearbook, China At the beginning of this century, scholar Wu Liangyong proposed the Greater Beijing Energy Statistical Yearbook, Beijing Statistical Yearbook, Tianjin Statistical Yearbook, Hebei Plan, which is usually regarded as the beginning of the integration of Beijing, Tianjin and Statistical Yearbook, Regional Statistical Yearbook, National Economic and Social Devel- Hebei. After that, the coordinated development of Beijing, Tianjin and Hebei has experi- opment Statistical Bulletin, China’s economic and social big data research platform, etc. enced three stages, namely, the three regions reached consensus on the cooperation of the The energy statistical yearbook reﬂects China’s energy construction, production, consump- Beijing-Tianjin-Hebei region, the initial formulation of regional development planning, tion, and the balance between supply and demand. The statistical yearbooks of various and the coordinated development of the Beijing-Tianjin-Hebei region was elevated to a provinces and cities reﬂect the annual data of local economic and social development. These national strategy level and its implementation was accelerated. Recently, Beijing issued data are mainly derived from the census, and are veriﬁed and corrected in comparison the “Implementation Plan on Establishing a More Effective New Mechanism for Coordi- with historical data, and they have a certain reliability. nated Regional Development”, which proposed that by 2035, the framework of Beijing- 3. System Dynamics Method Tianjin-Hebei world-class urban agglomeration will be basically formed. Therefore, this paper 3.1. tak Framework es 2000–20of 35the as t Model he research period. The following basic data were used: the total population of Beijing, Tianjin and Hebei Province are 1382, 1001.14 and 6674 million, re- The urban eco-efﬁciency evaluation system may contain several subsystems, which spectively. The emergy data of Beijing-Tianjin-Hebei resource stock are calculated by may be included in a larger system (urban agglomeration or country). Therefore, we emergy analysis. Updatable resources include solar energy, wind energy, rainwater chem- established a two-layer system dynamics model to study the eco-efﬁciency of urban ag- ical energy and potential energy, which provides driving forces for the ecological eco- glomerations. In this model, the ﬁrst layer is the spatial layout of the urban agglomeration. nomic system. Unable to update resources include oil, natural gas, etc. At the same time, Each urban system within the urban agglomeration is regarded as an element in the system, the area also imports goods and equipment from the outside, and exports sewage and so as to realize the overall analysis. The second layer is the smaller scale—urban scale. In garbage to the outside. addition to the inﬂuence of factors within the subsystem, there is population ﬂow among subsystems, The original as da shown ta used in in Figur thise s2 t.udy came from China Statistical Yearbook, China Energy Statistical Yearbook, Beijing Statistical Yearbook, Tianjin Statistical Yearbook, He- bei Statistical Yearbook, Regional Statistical Yearbook, National Economic and Social De- velopment Statistical Bulletin, China’s economic and social big data research platform, etc. The energy statistical yearbook reflects China’s energy construction, production, con- sumption, and the balance between supply and demand. The statistical yearbooks of var- ious provinces and cities reflect the annual data of local economic and social development. These data are mainly derived from the census, and are verified and corrected in compar- ison with historical data, and they have a certain reliability. Systems 2022, 10, x FOR PEER REVIEW 5 of 19 3. System Dynamics Method 3.1. Framework of the Model The urban eco-efficiency evaluation system may contain several subsystems, which may be included in a larger system (urban agglomeration or country). Therefore, we es- tablished a two-layer system dynamics model to study the eco-efficiency of urban agglom- erations. In this model, the first layer is the spatial layout of the urban agglomeration. Each urban system within the urban agglomeration is regarded as an element in the system, so as to realize the overall analysis. The second layer is the smaller scale—urban scale. In Systems 2022, 10, 61 5 of 17 addition to the influence of factors within the subsystem, there is population flow among subsystems, as shown in Figure 2. Figure 2. Framework description of the two-layer model of system dynamics. Figure 2. Framework description of the two-layer model of system dynamics. Some scholars have studied the inﬂuencing factors of eco-efﬁciency. Ou uses the Some scholars have studied the influencing factors of eco-efficiency. Ou uses the spa- spatial error model (SEM) to study the inﬂuencing factors of eco-efﬁciency, and found tial error model (SEM) to study the influencing factors of eco-efficiency, and found that that factors such as environmental regulation, economic development level, structural factors such as environmental regulation, economic development level, structural changes, opening to the outside world and urbanization all have a signiﬁcant impact changes, opening to the outside world and urbanization all have a significant impact on on eco-efﬁciency [29]. Qu uses the spatial lag model (SLM) to analyze the inﬂuencing eco-efficiency [29]. Qu uses the spatial lag model (SLM) to analyze the influencing factors factors of regional eco-efﬁciency. The results show that the regional economic development of regional eco-efficiency. The results show that the regional economic development level, level, state-owned proportion, foreign investment and R&D intensity have a positive effect state-owned proportion, foreign investment and R&D intensity have a positive effect on on the improvement of eco-efﬁciency level, while the increase in capital–labor ratio and the improvement of eco-efficiency level, while the increase in capital–labor ratio and the the proportion of export trade are not conducive to the improvement of eco-efﬁciency proportion of export trade are not conducive to the improvement of eco-efficiency level level [30]. Chen et al. use the spatial panel econometric model to explore the impact [30]. Chen et al. use the spatial panel econometric model to explore the impact of tourism of tourism economic development on regional eco-efﬁciency and its spatial effect. It is economic development on regional eco-efficiency and its spatial effect. It is found that in found that in the long-term development, tourism economic development and regional the long-term development, tourism economic development and regional eco-efficiency eco-efﬁciency shows a relatively obvious “Kuznets Curve” effect [31]. Tang et al. construct shows a relatively obvious “Kuznets Curve” effect [31]. Tang et al. construct a macroeco- a macroeconomic model with output loss and innovation compensation factors to prove nomic model with output loss and innovation compensation factors to prove that land that land urbanization has a negative impact on urban eco-efﬁciency, and the improvement urbanization has a negative impact on urban eco-efficiency, and the improvement of in- of industrial structure plays a positive mediating role between the two [32]. To sum up, dustrial structure plays a positive mediating role between the two [32]. To sum up, exist- existing studies have found that economic development level, industrial structure, urban ing studies have found that economic development level, industrial structure, urban pop- population size and density, energy structure, government environmental regulation and ulation size and density, energy structure, government environmental regulation and for- foreign direct investment have an impact on eco-efﬁciency. eign direct investment have an impact on eco-efficiency. This paper analyzes the inﬂuencing factors of eco-efﬁciency system with reference This paper analyzes the influencing factors of eco-efficiency system with reference to to the conceptual framework “Driving-Force-Pressure-State-Impact-Response” (DPSIR) the conceptual framework “Driving-Force-Pressure-State-Impact-Response” (DPSIR) rec- recommended by the United Nations Environment Programme (UNEP). The main “driving ommended by the United Nations Environment Programme (UNEP). The main “driving factors” affecting the eco-efﬁciency system of urban agglomerations are total change in factors” affecting the eco-efficiency system of urban agglomerations are total change in economy and population. The evaluation of eco-efﬁciency is mainly through the mea- economy and population. The evaluation of eco-efficiency is mainly through the measure- surement of “status and impact” indicators such as economic quality, resource supply, ment of “status and impact” indicators such as economic quality, resource supply, and and environmental impact. The “response” in the DPSIR framework mainly refers to environmental impact. The “response” in the DPSIR framework mainly refers to the fact the fact that decision-makers adjust policies and management methods and optimize the that decision-makers adjust policies and management methods and optimize the interaction of economy, society and environment by changing driving forces and pressure factors, which corresponds to the scenario analysis and policy simulation of eco-efﬁciency implementation. Based on this analysis framework, the dynamic simulation model of an eco-efﬁciency evaluation system is divided into currency ﬂow subsystem, energy logistics subsystem and population ﬂow subsystem, and the emergy evaluation index is integrated into the energy logistics subsystem. The currency ﬂow subsystem mainly focuses on the economic operation of the Beijing-Tianjin-Hebei region, studies the input and output of the industry, and the economic growth should be in response to the society and the environment. This model of this paper will focus on the impact of labor and ﬁxed assets on the economy. The subsystem of population ﬂow provides labor supply for economic development, and the increase in human capital has a positive effect on the economy. However, the increase in Systems 2022, 10, 61 6 of 17 population will mean that more living resources are consumed and more domestic garbage is discharged, which will have a negative impact on environment quality. The energy logistics subsystem is composed of material ﬂow (resource ﬂow) and energy ﬂow. Material ﬂow records the movement state and mutual transformation process of different kinds of substances in the system, and the energy ﬂow represents the process of energy transfer and consumption in the system. The energy logistics system provides resources and power for the development of the eco-efﬁciency system. The evaluation index system of eco-efﬁciency was based on the calculation of emergy ﬂows among several subsystems. In this paper, the SD model of eco-efﬁciency evaluation subsystem (city scale) was Systems 2022, 10, x FOR PEER REVIEW 7 of 19 established on the second level by sorting out the causal relationship of inﬂuencing factors of the sub-system, in order to overcome the limitations of the “black box” of the urban eco-efﬁciency system, as shown in Figure 3. Figure 3. The second-level SD model of city scale eco-efﬁciency assessment. Figure 3. The second-level SD model of city scale eco-efficiency assessment. The Beijing Social Governance Development Report (2015–2016) showed that the population flows frequently among the three regions of Beijing, Tianjin and Hebei, and the floating population of Hebei accounts for one-fifth of Beijing’s floating population, with an increasing trend year by year. The migration of population in Beijing-Tianjin-He- bei region not only leads to the disharmony of regional development, but also affects the environmental quality. Therefore, in the first-layer model (urban agglomeration scale), we consider the flow of population factors between cities, and the evaluation system of urban agglomeration eco-efficiency is shown in Figure 4 below. Systems 2022, 10, 61 7 of 17 The Beijing Social Governance Development Report (2015–2016) showed that the population ﬂows frequently among the three regions of Beijing, Tianjin and Hebei, and the ﬂoating population of Hebei accounts for one-ﬁfth of Beijing’s ﬂoating population, with an increasing trend year by year. The migration of population in Beijing-Tianjin-Hebei region not only leads to the disharmony of regional development, but also affects the environmental quality. Therefore, in the ﬁrst-layer model (urban agglomeration scale), we Systems 2022, 10, x FOR PEER REVIEW 8 of 19 consider the ﬂow of population factors between cities, and the evaluation system of urban agglomeration eco-efﬁciency is shown in Figure 4 below. Figure 4. The first-level SD model of eco-efficiency assessment of urban agglomeration scale. Figure 4. The ﬁrst-level SD model of eco-efﬁciency assessment of urban agglomeration scale. 3.2. Model Development and Formulas 3.2. Model Development and Formulas The subsystems of the ﬁrst-layer eco-efﬁciency module are its urban components: The subsystems of the first-layer eco-efficiency module are its urban components: Beijing, Tianjin and Hebei Province. Beijing, Tianjin and Hebei Province. The second layer gives the SD model of the eco-efﬁciency evaluation of each subsystem. The second layer gives the SD model of the eco-efficiency evaluation of each subsys- Figure 3 shows the stock ﬂow diagram of the subsystem, which includes population ﬂow, tem. Figure 3 shows the stock flow diagram of the subsystem, which includes population currency ﬂow and energy ﬂow. The total population is predicted from the previous year ’s flow, currency flow and energy flow. The total population is predicted from the previous total population, births, deaths, and immigration and emigration ﬁgures. An analysis of year’s total population, births, deaths, and immigration and emigration figures. An anal- trends in previous data on births and deaths reveals small changes in birth and death rates ysis of trends in previous data on births and deaths reveals small changes in birth and in the three regions. death rates in the three regions. Po pul ation = Po pul ation + Birth + Death + I mmigration + Outmigration Population jt= Population + Birth + jt Death j+ t Immigratio n j+ t Outmigrati on jt j(t1) jt j(t− 1) jt jt jt jt This expression is a numerical equation. We use it to describe how the total population This expression is a numerical equation. We use it to describe how the total popula- is calculated. Po pul ation represents the number of people in area j in the (t 1) year. j(t1) tion is calculated. Population represents the number of people in area j in the j(t−1) The relationship between regional GDP, labor force and capital was calculated with (t − 1) year. reference to the Cobb-Douglas production function. The Cobb-Douglas production function The relationship between regional GDP, labor force and capital was calculated with is a production function created by American mathematician Cobb and economist Douglas reference to the Cobb-Douglas production function. The Cobb-Douglas production func- tion is a production function created by American mathematician Cobb and economist Douglas when they discuss the relationship between input and output. The relationship between output (GDP) and input labor (L) and capital (K) can be expressed as follows: β μ GDP = A  K  L  e The index α represents the capital elasticity, indicating that when the production capital increases by 1%, the output increases by α% on average; β is the elasticity of labor force, which means that when the labor force input into production increases by 1%, Systems 2022, 10, 61 8 of 17 when they discuss the relationship between input and output. The relationship between output (GDP) and input labor (L) and capital (K) can be expressed as follows: a b m GDP = AK L e The index a represents the capital elasticity, indicating that when the production capital increases by 1%, the output increases by a% on average; b is the elasticity of labor force, which means that when the labor force input into production increases by 1%, the output increases by b% on average; A stands for comprehensive productivity and represents technological progress, and m is the random disturbance term. Among them, Labor = total population labor coefﬁcient Increase in ﬁxed assets = investment in ﬁxed assets 0.95. Since there will inevitably be some loss and waste in the process from the beginning to the ﬁnal use of an investment, which cannot reach 100% utilization, this paper assumes that the utilization efﬁciency of ﬁxed asset investment is 95%. The depreciation method of all assets adopts the straight-line method, and the depreciation rate of ﬁxed assets was set as 9.6% [33]. K = I /P + 1 d K j,t j,t j,t j,t jt1 K , I , P , d represent ﬁxed asset stock, ﬁxed asset investment amount, ﬁxed capital j,t j,t j,t j,t investment price index and ﬁxed capital depreciation rate in year t in j region. The initial stock of ﬁxed asset comes from the existing literature [34]. The equation in this paper is set based on the existing research and the research object. The main variable equations are shown in Table 1. Table 1. Relationship of main variables. Relational Formula Serial Number Beijing Tianjin Hebei Increment of ﬁxed assets = [76.491 Increment of ﬁxed assets = [146.44 Increment of ﬁxed assets = [469.89 2 2 1 (TIME-2000) 315.35 (TIME-2000) 327.4 (TIME-2000) + 602.66] 95% (TIME-2000) + 1079.1] 95% (TIME-2000) + 2016.5] 95% Depreciation of ﬁxed assets = ﬁxed Depreciation of ﬁxed assets = ﬁxed Depreciation of ﬁxed assets = ﬁxed assets 9.6% assets 9.6% assets 9.6% lg(GDP) = 1.899 + 0.823 lgL + lg(GDP) = 1.484 + 1.054 lgL + lg(GDP) = 9.573 + 3.296 lgL + 0.813 lgK 0.565 lgK 0.433 lgK 4 Labor = population labor rate Labor = population labor rate Labor = population labor rate IF population > 23,000,000, Immigrant population = population Immigrant population = population 5 Immigrant population = population 0.003 0.002 0.002; ELSE = population 0.02 Emigration population = population Emigration population = population Emigration population = population 0.001 0.002 0.002 Wastewater emergy Wastewater emergy Wastewater emergy value = population 2.574 value = population 1.53 value = population 8.32 14 14 1 3 10 /Person + GDP 6.95 10 /Person + GDP 1.92 10 /Person + GDP 4.23 8 9 9 10 /GDP 10 /GDP 10 /GDP Emergy value of exhaust gas = GDP Emergy value of exhaust gas = GDP Emergy value of exhaust gas = GDP 6 7 7 5.19 10 1.16 10 3.03 10 Emergy input = emergy_of_foreign_direct_investment + import_emergy + international_tourism_foreign exchange_earnings_emergy 9 Emergy reduction = emergy 0.05 + emergy output 10 Energy value of waste = exhaust gas emergy value + waste water emergy value 3.3. Calculation Method of Emergy The emergy analysis method regards the research system as an energy system, takes emergy as the benchmark, and transforms the heterogeneous and non-comparable energy as well as various non-energy forms such as energy ﬂow, capital ﬂow, information ﬂow and Systems 2022, 10, 61 9 of 17 population ﬂow in the system into the same standard emergy for processing and analysis. Since all kinds of energy come from solar energy, solar energy is often used to measure a certain energy value in emergy analysis [35]. The formula is as follows: E = E . m x E represents the emergy of a material or energy; E represents the number of joules of m x material or energy available; represents the conversion of the emergy value of a material or energy, or the amount of solar energy required to produce one joule of services or products (unit: sej/J or sej/g). The urban eco-efﬁciency emergy stream was divided into local renewable emergy (R), local non-renewable emergy (N), and imported emergy from external systems (IMP). In order to minimize the risk of double counting, this paper selects the maximum renewable ﬂow (sunshine, wind, rain, river and earth cycle) to calculate the renewable resource emergy of the Beijing-Tianjin-Hebei region. The solar conversion data were taken from previous studies [24,36–38]. The emergy values of the main variables in Beijing, Tianjin and Hebei province in 2000 are shown in Table A1 of the Appendix A. 3.4. Eco-Efﬁciency Evaluation Indicators Zhang and Yang constructed an indicator to evaluate the sustainable development ability of the system from the perspective of metabolism, namely the ecological efﬁciency index (UEI) [25]. It is a function of emergy yield ratio, emergy-value ratio of non-renewable resources and contaminant emergy ratio, the higher the ecological efﬁciency index, the higher the social and economic beneﬁts of the system under unit environmental pressure (see Table 2). Therefore, this paper makes a dynamic evaluation of the eco-efﬁciency of the Beijing-Tianjin-Hebei urban agglomeration by referring to the existing research and ecological efﬁciency index (UEI) [25,39,40], as well as based on the actual situation of the Beijing-Tianjin-Hebei urban agglomeration. Table 2. Emergy evaluation index system of eco-efﬁciency. Classiﬁcation Emergy Indicators Calculation Formula Unit The energy ﬂow Updatable Resource Emergy (R) R sej/a Non-updatable resource Emergy (N) N sej/a Import Emergy (IMP) I sej/a Export Emergy (EXP) E sej/a Waste emergy value (W) W sej/a Total emergy (U) R + N + IMP sej/a The energy efﬁciency Emergy Self-sufﬁciency Ratio (ESR) (N + R)/U % Emergy Waste Ratio (EWR) W/R % Environment load ratio (ELR) (U R)/R % Emergy Yield Ratio (EYR) (R + N + IMP)/IMP % Contaminant emergy ratio (W ) W/U % Non-updatable resource emergy ratio (N ) N/U % 2 2 Eco-efﬁciency index [25] UEI EYR (1 W/U) (1 N/U) - 4. Results and Discussion Taking 2000 as the base year, the time step is one year, and the operation cycle is 2000–2035, this paper used STELLA software to simulate the high-quality development level of Beijing-Tianjin-Hebei urban agglomeration. 4.1. Model Validity Veriﬁcation Model validity analysis is a necessary step of system simulation, which can be judged by comparing the difference between simulation value and existing statistical data. The system dynamics model constructed in this paper is a concrete abstract and approximate description of the real system. Whether the model can accurately present the real system is the key to the trend prediction and policy analysis of the system. Therefore, we judged the Systems 2022, 10, 61 10 of 17 reliability of the model by comparing the difference between the simulation value and the existing statistical data [41]. The inspection period of this article is from 2015 to 2018, and the selected indicators include population, GDP, etc. Since they are the main indicators for the result analysis, and they can be calculated with a subset of historical data, the feasibility of the actual inspection is ensured. The results show that there is a certain difference in the ﬁtting degree between the simulation data and the statistical data, which is directly related to the accuracy of historical data and the logical structure of the model itself. The relevant literature indicates that when the system dynamics model is used for trend prediction, the error is acceptable within 30% [42]. Therefore, effectiveness analysis in Table 3 shows that the model can accurately describe the high-quality development status of the Beijing-Tianjin-Hebei region and has a good prediction function. Table 3. Reliability test of the eco-efﬁciency simulation system of the Beijing-Tianjin-Hebei urban agglomeration. The Real Value Simulation Value Error Year Beijing Tianjin Hebei Beijing Tianjin Hebei Beijing Tianjin Hebei Total Population (10,000) 2015–2018 2167.25 1556.50 7492.75 2133.75 1489.67 7474.74 (0.0154) (0.0429) (0.0024) GDP (billion) 2015–2018 26,918.81 17,957.16 33,330.15 25,400.10 19,758.86 33,552.27 (0.0564) 0.1003 0.0067 International tourist foreign exchange 2015–2018 508,030 353,548 56,991.75 471,500 368,461.65 70,281.46 (0.0719) 0.0422 0.2332 earnings (US $10,000) Actual utilization of foreign investment (US 2015–2018 1,691,623 1,167,307 853,895.3 1,317,312 1,000,000 1,009,490.8 (0.2213) (0.1433) 0.1822 $10,000) Total exports (US $10,000) 2015–2018 5,979,500 5,773,075 3,221,700 5,679,745 6,796,826 2,948,552 (0.0501) 0.1773 (0.0848) Total import (US $10,000) 2015–2018 27,473,500 7,456,900 1,823,300 34,969,494 8,093,880 2,268,320 0.2728 0.0854 0.2441 4.2. Analysis of Simulation Results 4.2.1. Eco-Efﬁciency of Beijing-Tianjin-Hebei Urban Agglomeration The eco-efﬁciency index (UEI) is a sustainable development index that reﬂects urban resource efﬁciency, environmental efﬁciency and economic efﬁciency. As can be seen from Figure 5, although the Beijing-Tianjin-Hebei region advocates green production, the overall eco-efﬁciency index was not high, with an average of 0.3786 from 2000 to 2035. During the simulation period, the eco-efﬁciency of the Beijing-Tianjin-Hebei urban agglomeration showed a trend of increasing ﬁrst and then decreasing, reaching a maximum value in 2011. This is similar to the conclusion of Ren and Fang on the county-scale evaluation of eco-efﬁciency in the Beijing-Tianjin-Hebei urban agglomeration; that is, that the overall level of eco-efﬁciency is low, and most eco-efﬁciency values are below 0.4 [43]. This may be due to the fact that in the early stage of the development of the Beijing-Tianjin urban agglomeration, there were relatively few residents and resource-intensive industries, resulting in less waste discharge. With the continuous expansion of urbanization, the migration of residents and enterprises leads to the massive consumption of renewable resources such as hydropower, wind energy and geothermal energy, as well as the increase in waste emissions. Therefore, UEI shows a trend of rising ﬁrst and then falling. Next, we further analyzed the changes of the eco-efﬁciency index under different situations. As shown in Figure 6, when the actual utilization of foreign investment, interna- tional tourism foreign exchange earnings and import value of Beijing, Tianjin, and Hebei increase by 10%, it is conducive to the improvement of eco-efﬁciency. On the contrary, a 10% decrease in birth rate and ﬁxed asset investment of Beijing Tianjin and Hebei con- tributed to the increase in eco-efﬁciency. Previous studies have also shown that population agglomeration in the Beijing-Tianjin-Hebei region has a signiﬁcant negative impact on eco-efﬁciency [44]. Therefore, the Beijing-Tianjin-Hebei urban agglomeration must pay attention to the synchronous improvement of weight and quality in the process of intro- ducing actual utilization of foreign capital. In addition, tourism is a “smoke-free industry”, Systems 2022, 10, 61 11 of 17 and the foreign exchange income from tourism is compatible with the development of green industries, so it should be vigorously advocated. The data show that, in 2019, the international tourism foreign exchange income of Beijing, Tianjin and Hebei Province accounted for about 5.42% of the national international tourism foreign exchange income. At the same time, in the process of coordinated development of Beijing-Tianjin-Hebei, how Systems 2022, 10, x FOR PEER REVIE to Wadjust the population scale and reasonably arrange the ﬁxed asset investment to 12 achieve of 19 the improvement of eco-efﬁciency is a problem that needs to be discussed. Figure 5. Eco-efficiency index of Beijing-Tianjin-Hebei urban agglomeration from 2000 to 2035. Systems 2022, 10, x FOR PEER REVIEW 13 of 19 Figure 5. Eco-efﬁciency index of Beijing-Tianjin-Hebei urban agglomeration from 2000 to 2035. Next, we further analyzed the changes of the eco-efficiency index under different 0.40 situations. As shown in Figure 6, when the actual utilization of foreign investment, inter- national tour0. is 39 m foreign exchange earnings and import value of Beijing, Tianjin, and He- bei increase by 10%, it is conducive to the improvement of eco-efficiency. On the contrary, 0.38 a 10% decrease in birth rate and fixed asset investment of Beijing Tianjin and Hebei con- 0.37 tributed to the increase in eco-efficiency. Previous studies have also shown that popula- tion agglomeration in the Beijing-Tianjin-Hebei region has a significant negative impact 0.36 on eco-efficiency [44]. Therefore, the Beijing-Tianjin-Hebei urban agglomeration must pay 0.35 attention to the synchronous improvement of weight and quality in the process of intro- ducing actual utilization of foreign capital. In addition, tourism is a “smoke-free indus- 0.34 Internationa Actual try”, and the foreign exchange income from tourism is compatible with the development l tourist Original Fixed asset utilization Import Birth rate foreign of green industries, so it should be vigorously advocated. The data show that, in 2019, the value investment of foreign value exchange investment international tourism foreign exchange income of Beijing, Tianjin and Hebei Province ac- earnings counted for about 5.42% of the national international tourism foreign exchange income. key element+10% 0.3786 0.3777 0.3781 0.3804 0.3792 0.3968 key element−10% 0.3786 0.3795 0.3791 0.3767 0.3779 0.3592 At the same time, in the process of coordinated development of Beijing-Tianjin-Hebei, how to adjust the population scale and reasonably arrange the fixed asset investment to Figure 6. Changes of average eco-efficiency index from 2000 to 2035 under different simulation Figure 6. Changes of average eco-efficiency index from 2000 to 2035 under different simulation scenarios. scenarios. Note: The horizontal axis represents the parameters adjusted in different simulation achieve the improvement of eco-efficiency is a problem that needs to be discussed. scenarios. Note: The horizontal axis represents the parameters adjusted in different simulation scenarios. 4.2.2. Analysis of Eco-Efficiency Indicators of Beijing-Tianjin-Hebei Urban Agglomera- 4.2.2. Analysis of Eco-Efﬁciency Indicators of Beijing-Tianjin-Hebei Urban Agglomeration tion (1) Emergy waste ratio (EWR) (1) Emergy waste ratio (EWR) The emergy waste rate (EWR) is the ratio of waste emergy to renewable resource The emergy waste rate (EWR) is the ratio of waste emergy to renewable resource emergy, which is used to evaluate the availability of waste discharged by the system and emergy, whichth is e rused ecycling to cap evaluate acity of the the system availability . As shown in of Figwaste ure 7, thedischar waste ratged e of em by ergthe y is system and increasing from 2000 to 2035. The simulation results of emergy waste rate under different the recycling capacity of the system. As shown in Figure 7, the waste rate of emergy is scenarios show that when the birth rate and fixed asset investment in Beijing, Tianjin and increasing from 2000 to 2035. The simulation results of emergy waste rate under different Hebei province increase by 10%, the emergy waste rate of Beijing-Tianjin-Hebei urban agglomeration increases. scenarios show that when the birth rate and ﬁxed asset investment in Beijing, Tianjin and Hebei province increase by 10%, the emergy waste rate of Beijing-Tianjin-Hebei urban Original value 4.5 agglomeration increases. (Birth rate)+10% 3.5 (Birth rate)−10% (Fixed asset investment)+10% 2.5 (Fixed asset investment)−10% (Actual utilization of foreign investment)+10% 1.5 (Actual utilization of foreign investment)−10% (International tourist foreign exchange earnings)+10% 0.5 (International tourist foreign 0 exchange earnings)−10% (Import value)+10% (Import value)−10% time Emergy Waste Ratio Ecological efficiency index of Beijing- Tianjin-Hebei urban agglomeration Systems 2022, 10, x FOR PEER REVIEW 13 of 19 0.40 0.39 0.38 0.37 0.36 0.35 0.34 Internationa Actual l tourist Original Fixed asset utilization Import Birth rate foreign value investment of foreign value exchange investment earnings key element+10% 0.3786 0.3777 0.3781 0.3804 0.3792 0.3968 key element−10% 0.3786 0.3795 0.3791 0.3767 0.3779 0.3592 Figure 6. Changes of average eco-efficiency index from 2000 to 2035 under different simulation scenarios. Note: The horizontal axis represents the parameters adjusted in different simulation scenarios. 4.2.2. Analysis of Eco-Efficiency Indicators of Beijing-Tianjin-Hebei Urban Agglomera- tion (1) Emergy waste ratio (EWR) The emergy waste rate (EWR) is the ratio of waste emergy to renewable resource emergy, which is used to evaluate the availability of waste discharged by the system and the recycling capacity of the system. As shown in Figure 7, the waste rate of emergy is increasing from 2000 to 2035. The simulation results of emergy waste rate under different scenarios show that when the birth rate and fixed asset investment in Beijing, Tianjin and Systems 2022, 10, 61 12 of 17 Hebei province increase by 10%, the emergy waste rate of Beijing-Tianjin-Hebei urban agglomeration increases. Original value 4.5 (Birth rate)+10% 3.5 (Birth rate)−10% (Fixed asset investment)+10% 2.5 (Fixed asset investment)−10% (Actual utilization of foreign investment)+10% 1.5 (Actual utilization of foreign investment)−10% (International tourist foreign exchange earnings)+10% 0.5 (International tourist foreign 0 exchange earnings)−10% (Import value)+10% (Import value)−10% time Figure 7. Simulation results of emergy waste rate of the Beijing-Tianjin-Hebei urban agglomeration. (2) Contaminant emergy ratio The contaminant emergy ratio is the ratio of the sum emergy of the “three wastes” to the total emergy, which is used to measure the burden of waste on the entire system. The larger the contaminant emergy ratio is, the larger the amount of waste discharged from the system is, and the greater the degree of utilization of waste from the system is [45]. It can be seen from Figure 8 that the contaminant emergy ratio in the Beijing-Tianjin-Hebei region decreased ﬁrst and then increased during the simulation period. In the case of original value, the contaminant emergy ratio in 2011 and 2035 are 0.0097 and 0.026, respectively. The simulation results of the waste emergy ratio under different scenarios show that the contaminant emergy ratio increases when the birth rate and fixed asset investment increase by 10%, and the actual utilization of foreign investment, tourism foreign exchange income, and imports decrease by 10%. The sustainability of economic development is affected by the recycling rate of waste. Therefore, there are still some urgent tasks for environmental regulation, such as energy conservation under the guidance of urban transformation. (3) Emergy yield ratio (EYR) Emergy yield ratio (EYR) is an indicator that measures the contribution of system output to economic development. The higher the EYR, the higher the emergy return rate of the system. It also means under the same economic input, the higher emergy output will be obtained, that is, the higher the production efﬁciency of the system. As shown in Figure 9, the emergy yield ratio of the Beijing-Tianjin-Hebei urban agglomeration ﬂuctuated between 1.5 and 5.5 from 2000 to 2035, and has been on the rise since 2011, indicating that the economic efﬁciency of energy and resource utilization of Beijing-Tianjin-Hebei urban agglomeration has been improved recently. When the actual utilization of foreign capital, the foreign exchange income of international tourism and the import volume decreased by 10%, the emergy yield ratio increased. Emergy Waste Ratio Ecological efficiency index of Beijing- Tianjin-Hebei urban agglomeration Systems 2022, 10, x FOR PEER REVIEW 14 of 19 Figure 7. Simulation results of emergy waste rate of the Beijing-Tianjin-Hebei urban agglomera- tion. (2) Contaminant emergy ratio The contaminant emergy ratio is the ratio of the sum emergy of the “three wastes” to the total emergy, which is used to measure the burden of waste on the entire system. The larger the contaminant emergy ratio is, the larger the amount of waste discharged from the system is, and the greater the degree of utilization of waste from the system is [45]. It can be seen from Figure 8 that the contaminant emergy ratio in the Beijing-Tianjin-Hebei region decreased first and then increased during the simulation period. In the case of orig- inal value, the contaminant emergy ratio in 2011 and 2035 are 0.0097 and 0.026, respec- tively. The simulation results of the waste emergy ratio under different scenarios show that the contaminant emergy ratio increases when the birth rate and fixed asset investment increase by 10%, and the actual utilization of foreign investment, tourism foreign ex- change income, and imports decrease by 10%. The sustainability of economic develop- ment is affected by the recycling rate of waste. Therefore, there are still some urgent tasks Systems 2022, 10, 61 13 of 17 for environmental regulation, such as energy conservation under the guidance of urban transformation. 0.0325 Original value (Birth rate)+10% 0.0275 (Birth rate)−10% (Fixed asset investment)+10% 0.0225 (Fixed asset investment)−10% (Actual utilization of foreign 0.0175 investment)+10% (Actual utilization of foreign investment)−10% 0.0125 (International tourist foreign exchange earnings)+10% (International tourist foreign exchange 0.0075 earnings)−10% (Import value)+10% (Import value)−10% time Figure 8. Simulation results of contaminant emergy ratio of the Beijing-Tianjin-Hebei urban ag- Systems 2022, 10, x FOR PEER REVIEW 15 of 19 Figure 8. Simulation results of contaminant emergy ratio of the Beijing-Tianjin-Hebei urban glomeration. agglomeration. (3) Emergy yield ratio (EYR) Emergy yield ratio (EYR) is an indicator that measures the contribution of system 5.5 Original value output to economic development. The higher the EYR, the higher the emergy return rate of the system. It also means under the same economi (Birc th i n ra p te u )t +10% , the higher emergy output will be obtained, that is, the higher the production efficiency of the system. As shown in 4.5 (Birth rate)−10% Figure 9, the emergy yield ratio of the Beijing-Tianjin-Hebei urban agglomeration fluctu- ated between 1.5 and 5.5 from 2000 to 2035, and has been on the rise since 2011, indicating (Fixed asset investment)+10% that the economic efficiency of energy and resource utilization of Beijing-Tianjin-Hebei (Fixed asset investment)−10% urban agglomeration has been improved recently. When the actual utilization of foreign 3.5 capital, the foreign exchange income of international tourism and the import volume de- (Actual utilization of foreign creased by 10%, the emergy yield ratio increased. investment)+10% (Actual utilization of foreign 2.5 investment)−10% (International tourist foreign 2 exchange earnings)+10% (International tourist foreign exchange earnings)−10% 1.5 (Import value)+10% (Import value)−10% time Figure 9. Simulation results of emergy yield ratio of the Beijing-Tianjin-Hebei urban agglomera- Figure 9. Simulation results of emergy yield ratio of the Beijing-Tianjin-Hebei urban agglomeration. tion. (4) Environmental load ratio (ELR) (4) Environmental load ratio (ELR) Environmental load ratio (ELR) is the ratio of purchased and non-renewable local emergy Environmental load ratio (ELR) is the ratio of purchased and non-renewable local to free environmental emergy (renewable resource emergy). The environmental load rate emergy to free environmental emergy (renewable resource emergy). The environmental represents the pressure on the environment caused by the economic activities of the system [46]. load rate represents the pressure on the environment caused by the economic activities of When ELR < 3, the system environment bears less pressure and belongs to a healthy state; the system [46]. When ELR < 3, the system environment bears less pressure and belongs when 3 < ELR < 10, the system environment pressure is at a medium level and belongs to to a healthy state; when 3 < ELR < 10, the system environment pressure is at a medium sub-health state, and when ELR > 10, the system environment pressure is too high, which is level and belongs to sub-health state, and when ELR > 10, the system environment pres- an unhealthy state [25]. As shown in Figure 10, the environmental load rate of the Beijing- sure is too high, which is an unhealthy state [25]. As shown in Figure 10, the environmen- tal load rate of the Beijing-Tianjin-Hebei urban agglomeration showed an upward trend from 2000 to 2035, indicating that the pressure on the environment caused by system eco- nomic activities continued to increase. In the case of the original value, the average value of the environmental load rate from 2000 to 2035 is 94.57, which belongs to an unhealthy state. It shows that the pressure of urban ecosystem economic activities on the environ- ment in the Beijing-Tianjin-Hebei urban agglomeration is too large and does not weaken with the development of the city. The simulation results show that the environmental load rate is greatly affected by the amount of foreign capital, foreign exchange income from international tourism and imports, which is determined by the connotation of the environmental load rate. From the perspective of emergy analysis, a large number of emergy inputs from the outside and over-exploitation of local non-renewable resources are the main reasons of high environ- mental load rate. Contaminant emergy ratio Emergy Yield Ratio Systems 2022, 10, 61 14 of 17 Tianjin-Hebei urban agglomeration showed an upward trend from 2000 to 2035, indicating that the pressure on the environment caused by system economic activities continued to increase. In the case of the original value, the average value of the environmental load rate from 2000 to 2035 is 94.57, which belongs to an unhealthy state. It shows that the pressure of Systems 2022, 10, x FOR PEER REVIEW 16 of 19 urban ecosystem economic activities on the environment in the Beijing-Tianjin-Hebei urban agglomeration is too large and does not weaken with the development of the city. Original value (Birth rate)+10% (Birth rate)−10% (Fixed asset investment)+10% (Fixed asset investment)−10% (Actual utilization of foreign investment)+10% (Actual utilization of foreign investment)−10% (International tourist foreign exchange earnings)+10% (International tourist foreign exchange earnings)−10% （Import value)+10% （Import value)−10% time Figure 10. Simulation results of environmental load ratio of the Beijing-Tianjin-Hebei urban ag- Figure 10. Simulation results of environmental load ratio of the Beijing-Tianjin-Hebei urban agglom- glomeration. eration. 5. Conclusion and Suggestion The simulation results show that the environmental load rate is greatly affected by the In this paper, the emergy analysis and system dynamics method are combined to amount of foreign capital, foreign exchange income from international tourism and imports, establish the eco-economic system dynamics model of Beijing-Tianjin-Hebei urban ag- whg ic lh ome is d rat etie on rm biy n e ud sin bg y S th tel e lc ao s n of nto wa tatrie, on an od f tth he e e dn ev viel ro op nm me en ntt a slta lo tu as d an rad te m . F ot ro iv m atiton he of pe trh se p ective of e sm yse tem rgy ar an e a an lyal siy sze , adl a th rg rou e n gu hm sib mu erlo atfion em . e Tr h g ey resu inplu tst s sh fr ow om th th ate : (1) ou t F srio dm e a 2n 000 d o to v e 2035 r-ex,p th lo e itation eco-efficiency of the Beijing-Tianjin-Hebei urban agglomeration was not high, showing a of local non-renewable resources are the main reasons of high environmental load rate. trend of first rising and then falling. Compared with the value of eco-efficiency index in 5. Conclusions 2000, it increase and d bSuggestion y 13.28% in 2035. The analysis under different situations shows that the synchronous improvement of the quantity and quality of foreign capital actually uti- In this paper, the emergy analysis and system dynamics method are combined to lized, as well as the adjustment of population scale and rational arrangement of fixed as- establish the eco-economic system dynamics model of Beijing-Tianjin-Hebei urban ag- sets investment are conductive to the improvement of eco-efficiency; (2) The analysis of glomeration by using Stella software, and the development status and motivation of the various indicators of eco-efficiency of the Beijing-Tianjin-Hebei urban agglomeration system are analyzed through simulation. The results show that: (1) From 2000 to 2035, the shows that the emergy waste rate is rising, the environmental load rate is in an unhealthy eco-efﬁciency of the Beijing-Tianjin-Hebei urban agglomeration was not high, showing a state, and the decline of system emergy output efficiency because of the environmental trend of ﬁrst rising and then falling. Compared with the value of eco-efﬁciency index in pressure on the growth of imported emergy and non-renewable resource emergy is rising. 2000, it increased by 13.28% in 2035. The analysis under different situations shows that the Therefore, high environmental pressure, low re-use rate of pollutants and low production synchronous improvement of the quantity and quality of foreign capital actually utilized, efficiency of the system are important reasons for low eco-efficiency in regional economic development. According to the emergy analysis theory, if the Beijing-Tianjin-Hebei urban as well as the adjustment of population scale and rational arrangement of ﬁxed assets agglomeration wants to truly realize the high-quality development of economy, some fea- investment are conductive to the improvement of eco-efﬁciency; (2) The analysis of various sible approaches are to improve the utilization rate of renewable resources in the region, indicators of eco-efﬁciency of the Beijing-Tianjin-Hebei urban agglomeration shows that appropriately limit the input of external feedback emergy, and at the same time establish the emergy waste rate is rising, the environmental load rate is in an unhealthy state, and a recycling mechanism of waste resources and energy to improve the social and economic the decline of system emergy output efﬁciency because of the environmental pressure on benefits exchanged by unit resources, energy and environment. the growth of imported emergy and non-renewable resource emergy is rising. Therefore, This paper combines emergy analysis with the system dynamics method to show the high environmental pressure, low re-use rate of pollutants and low production efﬁciency of relationship between the system structure and factors through the system dynamics the system are important reasons for low eco-efﬁciency in regional economic development. model, and uses simulation technology to grasp the future high-quality development of According to the emergy analysis theory, if the Beijing-Tianjin-Hebei urban agglomeration urban agglomerations. In future work, more details can be considered in the model devel- wants to truly realize the high-quality development of economy, some feasible approaches opment to reduce the impact of data limitations and increase the integrity and authenticity areof to thimpr e systove em sthe imulutilization ation. rate of renewable resources in the region, appropriately Environmental load ratio Systems 2022, 10, 61 15 of 17 limit the input of external feedback emergy, and at the same time establish a recycling mechanism of waste resources and energy to improve the social and economic beneﬁts exchanged by unit resources, energy and environment. This paper combines emergy analysis with the system dynamics method to show the relationship between the system structure and factors through the system dynamics model, and uses simulation technology to grasp the future high-quality development of urban agglomerations. In future work, more details can be considered in the model development to reduce the impact of data limitations and increase the integrity and authenticity of the system simulation. Author Contributions: All authors contributed equivalently to this research. H.L. developed the original idea. All authors designed this study, collected, and analyzed the data. H.H. established the model and wrote the ﬁrst paper. X.B. provided advice on data collection, as well as reviewed and edited the manuscript. W.C. reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: The data presented in this study are available on request from the corresponding author. Conﬂicts of Interest: The authors declare no conﬂict of interest. Appendix A In this paper, the conversion rate of energy is mainly based on existing research [24,36–38], and the global energy reference line of 9.26 10 sej/year is used as the standard for conver- sion. The 2000 energy value analysis table of the Beijing-Tianjin-Hebei urban agglomeration is calculated, as shown in Table A1 below. Table A1. Main energy ﬂow analysis table of Beijing-Tianjin-Hebei Eco-economic system in 2000. Energy Solar Items Initial Data Conversion Energy Reference Rate Value Beijing Tianjin Hebei Beijing Tianjin Hebei 19 1 6 21 1 9 1 6 21 The solar energy 9.33 10 7.12 10 1.06 10 1.00 9.33 10 7.12 10 1.06 10 [24] 1 7 1 6 18 2 1 9 1 9 20 wind energy 1.28 10 9.77 10 1.46 10 6.32 10 8.09 10 6.17 10 9.22 10 [24] Chemical energy of 1 6 1 6 1 7 4 20 20 22 4.10 10 3.16 10 9.38 10 1.82 10 7.46 10 5.75 10 1.71 10 [24] Updatable rainwater resource energy The rain potential 15 14 1 6 3 1 9 18 20 2.54 10 2.07 10 7.58 10 8.89 10 2.26 10 1.84 10 6.74 10 [24] values (R) energy Earth rotation 1 6 1 6 1 7 4 20 20 21 1.65 10 1.26 10 1.88 10 2.90 10 4.79 10 3.65 10 5.45 10 [36] energy 21 21 22 subtotal 1.42 10 1.00 10 2.52 10 1 6 1 6 1 7 4 20 20 22 Surface soil loss 1.05 10 1.35 10 2.19 10 7.40 10 7.77 10 9.99 10 1.62 10 [24] 1 7 1 7 18 4 22 22 23 The raw coal (J) [24] 5.68 10 5.17 10 2.53 10 4.00 10 2.27 10 2.07 10 1.01 10 1 7 1 7 1 7 4 22 22 22 Crude oil (J) 3.15 10 2.97 10 3.12 10 5.40 10 1.70 10 1.6 10 1.69 10 [24] Non-updatable 6 6 7 15 22 21 22 cement (t) 8.27 10 2.68 10 4.69 10 2.07 10 1.71 10 5.55 10 9.71 10 [37] resource energy 16 16 16 4 21 21 21 Natural gas (J) 4.24 10 2.10 10 3.10 10 4.80 10 2.04 10 1.01 10 1.49 10 [38] values 6 6 7 1 5 21 21 22 steel (t) 6.97 10 3.16 10 1.31 10 1.40 10 9.76 10 4.42 10 1.83 10 [24] 1 7 16 1 7 5 22 22 22 Thermal power [36] 1.38 10 8.42 10 2.91 10 1.60 10 2.21 10 1.35 10 4.66 10 22 22 23 subtotal 9.15 10 6.22 10 2.98 10 1 0 9 9 12 22 22 21 imports 3.74 10 8.53 10 1.53 10 2.50 10 9.36 10 2.13 10 3.82 10 [38] International tourist 9 8 8 12 21 20 20 foreign exchange 2.77 10 1.42 10 2.32 10 2.50 10 6.93 10 3.55 10 5.80 10 [38] Import Emergy earnings Actual utilization of 20 21 21 21 3.01 10 2.56 10 1.39 10 2.50 10 7.53 10 6.40 10 3.48 10 [38] foreign investment 23 22 21 subtotal 1.08 10 2.81 10 7.88 10 1 0 9 9 12 22 22 21 Export Emergy exports 1.20 10 8.63 10 3.71 10 1.46 10 1.75 10 1.26 10 5.42 10 [38] 1 3 1 3 1 4 4 1 8 1 8 1 9 Waste gas 7.74 10 4.20 10 2.37 10 4.80 10 3.72 10 2.01 10 1.14 10 [36] Waste energy 1 5 1 5 1 5 5 21 21 21 Waste water [24] 4.47 10 2.04 10 7.40 10 8.60 10 3.85 10 1.75 10 6.36 10 value 21 21 21 subtotal 3.85 10 1.76 10 6.37 10 Systems 2022, 10, 61 16 of 17 References 1. 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Systems – Multidisciplinary Digital Publishing Institute

Published: May 8, 2022

Keywords: eco-efficiency; two-layer system dynamics; emergy analysis; Beijing-Tianjin-Hebei urban agglomeration

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Eco-Efficiency Assessment of Beijing-Tianjin-Hebei Urban Agglomeration Based on Emergy Analysis and Two-Layer System Dynamics

Eco-Efficiency Assessment of Beijing-Tianjin-Hebei Urban Agglomeration Based on Emergy Analysis and Two-Layer System Dynamics

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Eco-Efficiency Assessment of Beijing-Tianjin-Hebei Urban Agglomeration Based on Emergy Analysis and Two-Layer System Dynamics

Eco-Efficiency Assessment of Beijing-Tianjin-Hebei Urban Agglomeration Based on Emergy Analysis and Two-Layer System Dynamics

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