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Pathways towards regional circular economy evaluated using material flow analysis and system dynamics
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Pathways towards regional circular economy evaluated using material flow analysis and system dynamics Chengkang Gaoa,*, Chengbo Gaoa, Kaihui Songb, Kejing Fangc,* a
SEP Key Laboratory of Eco-Industry, Northeastern University, Shenyang, Liaoning, 110819, China Department of Geographical Science, University of Maryland, College Park, 20742, MD, USA c Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou, 510640, China b
ARTICLE INFO
ABSTRACT
Keywords: Circular economy MFA System dynamics Coupling model Evaluation indexes system
Circular Economy (CE) offers insights to sustainable production and consumption by integrating environmental analysis to the socioeconomic system. To promote sustainable development in Guangdong Province, this study integrates System Dynamics (SD) and Material Flow Analysis (MFA) into CE theories to establish a framework to comprehensively evaluate regional economies. Nine development scenarios are further developed to provide strategic recommendations for the development of CE in Guangdong. The results of these scenarios show that the CE of Guangdong is most effective among the nine scenarios when the birth rate is reduced by about 2‰, the growth rates of the primary and secondary industries are reduced by 2%, and the growth rate of the tertiary industry increased by 2%. The biological substance consumption, fossil fuel consumption, building mineral consumption, industrial exhaust emissions and solid waste emissions are 88.39 Mt, 86.63 Mt, 108.16 Mt, 280.90 Mt, and 69.02 Mt respectively. The total material input (TMI) of 10,000 RMB of GDP and the total material output (TMO) of 10,000 RMB of GDP are 49.64 kt/10,000 RMB and 42.70 kt/10,000 RMB in 2022 respectively. Based on the status quo and simulated results, this study acknowledges the importance of population control and highlights the vigorous development of tertiary industry in economic construction. Policy interventions such as building pilot demonstrative smart cities and industrial parks would facilitate long-term sustainability of urban systems.
1. Introduction As early as the end of the 1960s, American economist Boulding first proposed the Spaceship Earth theory, which led to the emergence of CE (Boulding, 1966). Since then, the development of CE has undergone three stages (Zhu, 2017). The first stage was a preliminary exploration stage, from 1966 to 1992, when some ideas and concepts of CE were proposed, emphasizing the importance of environmental sustainability of economic development (Commoner et al., 1997; Pearce and Turner, 1990). Meadows et al. (1992, p.1204) believed that human beings would eventually move toward CE development through unremitting efforts. The second stage was from 1992 to 2010 when increased theoretical model divergent research came on stage. Researchers began to think about how to switch from linear economy mode to a dynamic closed-looped system from an operational sense, and put forward a new CE mode with
various socio-environmental implications, intending to achieve green development from production and consumption. During this period, some research explored the internal relationship between the ecological environment and the development of CE at different levels (Mo et al., 2009; Wen et al., 2007; Gibbs, 2010; Mirata, 2004). In Germany, the CE concept was introduced into environmental policy to address issues related to the consumption of raw materials and natural resources (Winans et al., 2017). Andersen (2007, p.133) analyzed environmental policy priorities through external environmental pricing and provided theoretical support for achieving sustainable development of the CE. CE had phased into the third stage since 2010 with a flourishment of theoretical integration and application. Some scholars and entrepreneurs made joint efforts to apply the concepts of CE to enterprise development, promoting the application of CE at the enterprise level (de Oliveira et al., 2018; Sousa-Zomer et al., 2018). Singh et al. (2018, p.313) studied the readiness and acceptance of CE for micro, small and
Abbreviation: CE, circular economy; SD, system dynamics; MFA, material flow analysis; PCA, principal component analysis; AHP, analytic hierarchy process; DMI, direct material input; TMI, total material input; HF(IF), hidden flow (indirect flow); DMO, direct material output; HF(OF), hidden flow (indirect flow); TMO, total material output; COD, chemical oxygen demand ⁎ Corresponding authors. E-mail addresses: [email protected] (C. Gao), [email protected] (K. Fang). https://doi.org/10.1016/j.resconrec.2019.104527 Received 22 October 2018; Received in revised form 28 September 2019; Accepted 29 September 2019 0921-3449/ © 2019 Elsevier B.V. All rights reserved.
Please cite this article as: Chengkang Gao, et al., Resources, Conservation & Recycling, https://doi.org/10.1016/j.resconrec.2019.104527
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medium-sized enterprises. Popa and Popa (2017) proposed a framework for startup businesses to apply the theory of CE innovation. Meanwhile, the number of related research articles on CE had increased sharply (Ghisellini et al., 2016; Lieder and Rashid, 2016; Martins, 2018; Vladimirova, 2017; Saidani et al., 2018; Laso et al., 2018). In 2016, Nature published a series of special articles on CE when Boulding proposed this concept for 50 years (Stahel, 2016). The CE theory was introduced into China in 1977 and attracted the attention of the Chinese government in the late 1990s (Xue et al., 2010). Since the 20th century, the Chinese government had promulgated a series of CE policies, which triggered the development of CE at three levels – enterprises, regional and national levels (Li et al., 2010; Mathews and Tan, 2011). In 2008, China became the first country to adopt a Circular Economy Promotion Law (Winans et al., 2017). The practice of CE was also widely promoted and advanced by modern enterprises. Since 2001, the eco-industrial parks and CE demonstration parks had been deployed by the Chinese government coupled with continuous efforts of innovative businesses. In 2005, China launched the first batch of CE pilot projects. At the end of 2007, the second batch of national CE pilot projects was promoted, as strong support and significant extension of the practice of CE (He, 2009). Intensive research focused on advancing the rigor and flexibility of models used for CE as modern complexity being added to the humanenvironmental system. By the beginning of the 21st century, various perspectives and insights enriched research of the circular economy. Some scholars had improved CE index system based on MFA and SD (Moriguchi, 2007; Nakamura and Kondo, 2018; Jacobi et al., 2018). Geng et al. (2013, p.1526) proposed to complete and improve the CE indicator system by including socio-economic and demographic indicators, coupled with environmental accounting tools such as MFA and lifecycle assessment (LCA), and include, to comprehensively assess the regional circular economy. The practice of CE evaluation also highlighted the incorporation of decision-making approaches such as PCA (Yang et al., 2011) and AHP (Zhou et al., 2010) to inform the policymaking process. Zhang et al. (2017, p.238) used three-stage data envelopment analysis to study the interaction between dynamic industrial eco-efficiency evaluations and economic development in 30 provinces, in order to bridge relevant knowledge gaps. Jiang (2011, p.125) used fuzzy comprehensive method to evaluate the circular economy development level in Jiangsu, Heilongjiang and Qinghai, which provided an analytical framework as well as an empirital analysis of regional evaluation. The nationwide advocacy on sustainable development drove the flourish of CE in China. However, solving the dynamic real-world CE problems increasingly requires a widely acknowledged framework and integrated models to accommodate dynamic yet concrete data inputs, meanwhile, the incorporation of socio-economic and demographic indicators into the system. To this end, this study proposed a framework to quantify and comprehensively evaluate the implementation and effectiveness of regional CE by the combination of MFA and SD approach. The regional analytical framework is further applied to Guangdong Province as a case study. The comprehensive analytical framework of regional CE developed in this study could provide references for future research and development strategies to achieve long-term sustainable production and consumption.
flow of “resource-product-pollution emission” to a closed-loop process of “resource-product-renewable resources”. (2) System Dynamics is an interdisciplinary branch of systematical analysis and management science that studies the behavior of a system through system feedback. SD can also be used to analyze complex system problems that include societal, economic and ecological spheres (Hjorth and Bagheri, 2006). The overarching equation of SD is as follows. (1)
X = f (X , U , T )
where X Rm ； U Rr ; R is Euclidean space; m and r represent the dimensions of Euclidean space. SD can provide a transparent and integrative model that helps people understand the internal structure of the system, capture the behavior of the system, and effectively simulate the actual system to obtain the most scientific and appropriate decision scheme. (3) Material Flow Analysis is proposed on the bases of industrial metabolism theory and social metabolism theory. It is widely used to analyze the amount and direction of the material input and output in a certain region. MFA follows the law of conservation of mass and can describe the material input and output paths of the relevant system. MFA serves as not only the link between the human-social-economic system and ecological environment, but also a significant approach to regulate and control the coordinated development of economy and ecological environment to pursue sustainable development. The quantification and interpretation of indexes offer important tools in the process of MFA. Through the analysis of indexes, policymakers can understand the relationship between economic and environmental development within a country or region, and thus develop scientific and rational management policies. The region is a relatively open system, in which the material flow not only exchange with the outside world, but also can be self-contained to some extent. Therefore, the input account of the region contains internal direct input accounts, import input, and hidden flows. The output account of the region contains direct output accounts and export material flow, as well as export hidden flows. For comprehensive consideration, the following MFA evaluation indexes are selected, as shown in Table 1. The intensity efficiency indexes of MFA are mainly expressed by the indexes of material consumption intensity, which are used to measure the resource consumption per capita or the resource consumption per 10,000 RMB of GDP in the economic system. The intensity efficiency indexes of MFA are mainly affected by the total economic volume, population base, and industrial structure. To better reflect the input and output intensity, the following indexes are determined to characterize the input and output intensity efficiency of material flows: DMI per capita, TMI per capita, DMO per capita, TMO per capita, DMI of 10,000 RMB of GDP, TMI of 10,000 RMB of GDP, DMO of 10,000 RMB of GDP, and TMO of 10,000 RMB of GDP.
Table 1 Evaluation indexes and calculation method of MFA.
2. Theory and method 2.1. The related method of constructing MFA-SD model (1) Circular Economy is an emerging economic pattern and social development model, which centers on environmental protection, the full reuse of resources and the recycling of waste. CE takes “Reduce, Reuse, Recycle” as the yardstick, features with low consumption, low emission, and high efficiency of economic development (Kirchherr et al., 2017; Reike et al., 2018). It promotes the transformation from traditional linear economic development characterized by a one-way
Index classification
Index
Computational formula
Input
DMI TMI HF(IF)
Output
DMO
DMI = regional mining + regional imports TMI = DMI + HF(IF) HF(IF) = regional hidden flow + import hidden flow DMO = gas output + solid waste + regional exports HF(OF) = regional hidden flow + export hidden flow TMO = DMO + HF(OF)
HF(OF) TMO
2
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Fig. 1. Coupling of MFA-SD.
subsystem. Conversely, the socioeconomic subsystem and the intensity efficiency subsystem have a negative feedback relationship. In the same way, we can get the feedback relationship on the rest of the loop.
2.2. MFA-SD model description The coupling of MFA and SD model can provide a research framework to effectively analyze the human-environmental interaction and its sustainability, facilitating policy formulation which would be suitable for regional CE development.
2.2.3. Construction of total stock-flow diagram of MFA-SD model system The MFA-SD model combines the intensity efficiency indexes of the input and output in material flow accounting, indexes of MFA, and regional CE evaluation indexes in the material flow accounting. These indexes are integrated into each subsystem. The four subsystems are organically combined to form a relatively complete simulation model of CE, as shown in Fig. 3. The output of the secondary industry in the socioeconomic subsystem drives the industrial exhaust and solid waste. The subsystem is thus connected with the growth of industrial exhaust, solid waste and COD in the environmental impact subsystem through the output variables of the secondary industry. In the same way, the intensity efficiency subsystem is associated with the resource consumption subsystem through variables such as biological substance consumption and fossil fuel consumption. The environmental impact subsystem is connected with the intensity efficiency subsystem through industrial exhaust emissions, solid waste emissions, and regional exports. The socioeconomic subsystem is related to the intensity efficiency subsystem through variables such as population and GDP. Therefore, the four subsystems are interconnected through internal relationships. The flow chart of the system consists of 15 level variables, 16 rate variables, 5 constant variables, 14 auxiliary variables, and 11 lookup functions, described by 61 mathematical equations. The stock-flow diagram of the system is shown in Fig. 3, and the equations and parameters of the four subsystems are shown in Appendix Table B1, Table C1, Table D1, and Table E1.
2.2.1. Theory of MFA-SD model The MFA method can clearly reflect the connotation of CE that allows a detailed description of the amount and direction of materials consumed in the conceptual system, instead of a black box. SD contributes to revealing the cause-effect relationships by scrutinizing the internal structure of the system. The MFA-SD coupling model is shown in Fig. 1. The MFA-SD coupling model uses SD to connect thirteen indexes of MFA to form a regional CE evaluation model. The thirteen indexes are DMI, TMI, HF, DMO, TMO, DMI per capita, TMI per capita, DMO per capita, TMO per capita, DMI of 10,000 RMB of GDP, TMI of 10,000 RMB of GDP, DMO of 10,000 RMB of GDP, and TMO of 10,000 RMB of GDP (as shown in Appendix Table A1). 2.2.2. Construction of MFA-SD model According to the material flow evaluation indexes and the characteristics of CE, this study selects 20 indexes for the comprehensive evaluation of CE in Guangdong Province. Afterward, these indexes are linked by the SD theory and assigned to four subsystems. The four subsystems are as follows: socioeconomic subsystem, resource consumption subsystem, intensity efficiency subsystem, environmental impact subsystem. The socioeconomic subsystem consists of birth rate, death rate, and the growth rates of primary, secondary, and tertiary industry. Resource consumption subsystem includes fossil fuel consumption, biological substance consumption, building mineral consumption and hidden flows such as indirect effects from regional transactions and imports. Intensity efficiency subsystem includes the DMI of 10,000 RMB of GDP, TMI of 10,000 RMB of GDP, DMO of 10,000 RMB of GDP, TMO of 10,000 RMB of GDP, DMI per capita, TMI per capita, DMO per capita and TMO per capita. Environmental impact subsystem includes industrial exhaust emissions, solid waste emissions, and COD emissions. We take 2007 as the base year for its relatively complete data and run a 15-year model. According to the rule of thumb, the time step size selected in this paper is one year, as shown in Fig. 2. Fig. 2 presents positive and negative relationships between the four subsystems and the regional CE. The growth of the three major industries in the socioeconomic system will contribute to the development of regional CE, but at the same time will increase the emissions of various substances in the environmental impact subsystem; accordingly, the indexes in the intensity efficiency subsystem will decrease with the population grows. Therefore, there is a positive feedback relationship among the socioeconomic subsystem, regional CE and environmental impact
2.3. Data collection and processing 2.3.1. Data source This paper finds information on the annual consumption of various substances such as biomass consumption, building mineral consumption, etc. through the Guangdong Statistical Yearbook, China Statistical Yearbook, China Industrial Economics Statistical Yearbook, and China Energy Statistics Yearbook. The information is processed according to relevant research. 2.3.2. Processing method (1) The regional material flow accounting system is based on the EU guidelines (2001, p.9) and is consistent with the MFA method of Eurostat in light of regional realities. (2) For locally mined and produced materials, only primary raw materials are counted (secondary products excluded), to avoid 3
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Fig. 2. Causality of development of CE with MFA-SD model.
omission and double counting. (3) Import and export commodities that are not measured by weight in the yearbook shall be converted into weight units for consistency. (4) The production of aquatic and livestock products that are cultivated and fed with agricultural products shall be treated as material stocks.
the gas input, R is the regional imports, B is the biological substance production, and N is the non-biological substance production. Biological substances mainly include agriculture, forestry, animal husbandry, and fishery. The animal husbandry sector is represented by flows instead of stock due to data limitation. The fishery is measured by wild-caught fish, while farmed fish fed with agricultural products are not counted. Non-biological substances refer to fossil fuels and building minerals. Fossil fuels mainly include four major categories: raw coal, crude oil, natural gas, and electric power. The building minerals mainly include bricks, tiles, stones, sand, and boulder; the total weight of building minerals can be calculated by the annual completion area of the building. Air inputs include oxygen consumption of fuel combustion, oxygen consumption of human and biological respiration. The photosynthesis of plants in the natural environment, the O2 required for the biology and soil respiration and the CO2 emissions are not directly involved in human economic activities, thus not being counted. The oxygen consumption of fuel combustion and the oxygen consumption of human and biological respiration need to be estimated.
2.4. Methods of material flow accounting 2.4.1. Accounting of material flow input The input of the region includes three parts: regional mining, air input, and regional imports. The regional mining mainly refers to the biological substance production and non-biological substance production.
I = Mj + Gin + R
(2)
Mj = B + N
(3)
where I represents total input in a region, Mj is the regional mining, Gin is
Fig. 3. Total stock-flow diagram of regional CE based on MFA-SD model. 4
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Fig. 4. Parameters of nine scenarios.
In this study, the amount of O2 consumed in fuel combustion (Q) was calculated by the following formula (Yang, 2013).
Q = S × 0.5 + C × 0.75
that is harvested from the crops not involving commodity economic activities, mainly represented by crop straw. In this study, the amount of crop straw unutilized is used to estimate the amount of crop residues. The hidden flow of fossil fuels mainly refers to the ecological burden caused by the exploitation of raw coal, crude oil and natural gas, which directly or indirectly affects the environment. The import and export hidden flow can be calculated by multiplying the main commodity of the import and export by the corresponding hidden flow coefficient. The topsoil movement caused by the infrastructure construction mainly includes the land taken from houses excavation, transportation, and water conservancy facilities. The hidden flow of infrastructure construction is calculated by the amount of excavation of building construction; moreover, the amount of excavation of the building is estimated by the following formula (Zhou et al., 2006).
(4)
where S is the emission of SO2 , C is the emission of CO2 . The estimation of annual oxygen consumption by human-beings is based on an averaged respiration rate of 7–8 liters of air per minute during rest, while those consumed by livestock is based on the year-end output, mainly including cattle, pigs, sheep and, poultry. Imported substances outside the region mainly include biological substances, non-biological substances, minerals, industrial raw materials, consumables, finished products, and semi-finished products. 2.4.2. Accounting of material flow output The output of the region includes solid waste, gas output, and regional export.
O = W + Gout + E
=
(5)
2.5. The design of model simulation scenarios Nine scenarios are developed with various conditions to provide options for development in seek of strategic and flexible policy to promote CE in Guangdong Province. An optimal scenario among the nine is also obtained that describes an ideal yet relatively feasible development mode This study selects the birth rate, the growth rate of the primary industry, the growth rate of the secondary industry and the growth rate of the tertiary industry as the control variables. Four parameters varied in different scenarios (as shown in Fig. 4) with simulation to 2022. The sum of growth rates of the three major industries is consistent with the projected growth of GDP, but the share of each industry varies accordingly. Due to the rapid development of the tertiary industry, the growth rate of the output value of the tertiary industry will be increased, and the growth rate of the output value of the primary and second industries will be lowered. For the stable and healthy development of CE, the adjustment scope of each variable should not be too large. After comprehensive consideration, each variable is adjusted by two units according to its original unit.
2.4.3. Accounting of hidden flow Hidden flows mainly consist of regional hidden flow, import, and export hidden flows. Regional hidden flows ( ) are the sum of flow of agricultural residues, topsoil movement of infrastructure construction and fossil fuel consumption. Import and export hidden flows refer to hidden flows caused by import and export commodities, such as the total amount of material consumed during the mining or transportation of imported and exported metals and non-metals.
+
+
+
(7)
In the equation, represents the regional amount of excavation of the building construction and is the construction completion area of the year.
where O represents the total output of the region, W is the solid waste, Gout is the gas output, and E is the regional export. Solid waste mainly includes industrial solid waste and domestic garbage. The gas output mainly includes sulfur dioxide, carbon dioxide, nitrogen oxides, and chimney, in which carbon dioxide only counts emissions from fossil fuel combustion. Export substances mainly include biological substances, non-biological substances, minerals, industrial raw materials, energy raw materials, consumables, finished products, and semi-finished products.
=
× 3.2 × 1.55
(6)
where is the total hidden flow, α is the biological substance hidden flow, is the infrastructure construction hidden flow, is the fossil fuel hidden flow, and is the import and export hidden flow. The hidden flow of biological substance mainly refers to the waste 5
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Fig. 5. Material flow change in Guangdong Province during 2007–2014. (a): input, (b): output, (c): hidden flow.
3. Results and discussion
trends exist among the biological substance hidden flows, the import hidden flows, and the export hidden flows. The total hidden flow of fossil fuel was 29–38 times as large as its corresponding consumption because of the large coefficient of the fossil fuel hidden flow. As the consumption of building minerals in the input increased from 104.86 Mt in 2008 to 356.38 Mt in 2009, the corresponding hidden flow increased 3.4 times, from 273.45 Mt to 92.94 Mt.
3.1. Analysis of accounting results 3.1.1. Analysis of the input, output and total hidden flows of material flow Fig. 5(a) shows the changes in input. It can be seen that the biological substance, fossil fuels, and outsourcings are generally in increasing trajectories. The air input had slowly increased from 123.33 Mt in 2007 to 165.09 Mt in 2014, with an average annual growth rate being about 4%. The financial crisis slightly negatively affected construction industry when it broke out, but real estate companies made use it as a business opportunity given the cultural factors and policy interventions. This led to the dramatic increase in construction during 2008-2010. The net flow of infrastructure started to decrease sharply during 2009–2010 due to regulation from the central government. Fig. 5(b) shows the changes in output. The regional export increased from 5.96 Mt in 2007 to 9.81 Mt in 2009 and, afterward, decreased to 6.04 Mt in 2014. Solid waste generally showed an upward trend between 2007 and 2012, with an average annual growth rate of 9%. It declined slightly in 2009 and then stabilized. The gas output was gradually increasing, with an average annual growth rate of 5%, and the growth rate from 2010 to 2014 was slower than that of 2007–2009. Fig. 5(c) shows the changes in hidden flows. Similar increasing
3.1.2. Analysis of material flow evaluation indexes Fig. 6(a) shows that DMI of Guangdong Province increased from 241.74 Mt in 2007 to 521.75 Mt in 2014, with an average annual growth rate of 12%. Building materials are the major contributor to DMI, representing 42% of the DMI in 2007 to 71% in 2010. The rapid development of real estate in China resulted in a sharp increase in building minerals demand.(Fig. 6 should be at the top of this paragraph) Fig. 6(b) indicates that the regional hidden flow is a dominant component in the TMI, representing 76–81% of the TMI during the study period. It could be explained by the large coefficient of fossil fuel consumption in the hidden flow, which means that fossil fuels mining would bring a huge ecological burden to the natural environment. Fig. 6(c) and (d) show that the gas output is the major contributor to DMO, reaching a maximum of 76% of the total, while the regional hidden flows contributed 87–91% to TMO during the study period. 6
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Fig. 6. The changes in DMI, TMI, DMO, and TMO based on MFA in Guangdong, China. (a): DMI, (b): TMI, (c): DMO, (D): TMO.
mining will lower the gas output and the hidden flow in the TMI and TMO thus plays an important role in the development of local CE. 3.2. Comprehensive evaluation of CE in Guangdong Province The comprehensive evaluation of regional CE is helpful to examine the problems existing in the development of regional social economy, to adopt effective regulatory measures to make the CE gradually develop towards the direction of high quality and high efficiency. According to the evaluation index of each subsystem, the overall development of CE is analyzed.(Fig. 7 should be below this paragraph) It can be seen from Fig. 7 that the index of socioeconomic subsystem showed wavy changes, mainly affected by the economic crisis from 2008 to 2009, decreased from 0.241 in 2008 to 0.074 in 2009. The index of the resource consumption subsystem showed a decreasing trend. With the development of society and economic growth, the demand for resources increased year by year, leading to the decline of the index of the resource consumption subsystem. The index of intensity efficiency subsystem increased from 0.134 in 2007 to 0.382 in 2014, indicating that the resource and environmental cost of economic development decreased over time, which promoted the development of
Fig. 7. Comprehensive evaluation index of CE in Guangdong Province.
In summary, attention should be attached to the theoretical basis and essential requirements of the CE, especially reducing the consumption of fossil fuels by reducing mining activities. The reduction of 7
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Fig. 8. The change of indexes in nine scenarios in 2022.
fuel consumption: Scenario 1 consumes the most fossil fuels, reaching 109.00 Mt, and scenario 8 is the least, only 86.63 Mt. (3) Building mineral consumption: Scenario 8 consumes the least amount of 1081.56 Mt, and scenario 2 consumes 3.74 times of scenario 8, up to 4043.97 Mt. (4) TMI of 10,000 RMB of GDP: Scenario 3 is the highest, with 69.97 kt/10,000 RMB, and scenario 5 is the lowest, at 48.90 kt/ 10,000 RMB. (5) TMO of 10,000 RMB of GDP: The TMO of 10,000 RMB of GDP in scenario 3 is 45.72 kt/10,000 RMB, the highest among the nine scenarios, and scenario 5 is the lowest, only 41.84 kt/10,000 RMB. (6) Industrial exhaust emissions: Scenario 8 has the lowest emissions of only 280.90 Mt, and scenario 1 is 1.12 times that of scenario 8, which is 340.89 Mt. (7) Solid waste emissions: Scenario 3 generates the highest (78.99 Mt) solid wastes while scenario 8 generates the lowest (69.02 Mt).(Fig. 8 should be at the top of this paragraph) Considering the impact of the above eight indexes on different scenarios, only the total GDP is positively related to the CE, and the rest are negatively associated with the CE. The optimal scenario is featured with high total GDP and low other indexes. After comparison, scenario 8 is the most conducive to the development of CE. That is, when the birth rate is reduced by 2‰, the growth rate of the primary industry and the secondary industry are both reduced by 2%, and the growth rate of the tertiary industry is increased by 2%, which could best lead to CE.
CE. The index of the environmental impact subsystem decreased significantly first and then rose slightly. Based on the impact of the four subsystems on the comprehensive evaluation index, the comprehensive evaluation index of CE in Guangdong Province fluctuated greatly, which increased from 0.649 in 2007 to 0.691 in 2008, dropped down to 0.420 in 2009, reached a plateau around 0.544 in 2011; afterwards, showed a steady increase from 2012 (0.471) to 2014 (0.495).(Fig. 7 should be at the top of this paragraph) To sum up, the main problems in the current CE in Guangdong Province are the excessive consumption of resources, increasing emissions of pollutants, and the unstable development of CE. Therefore, the key to the development of CE in Guangdong Province is to control the consumption of resources, improve the intensity efficiency, reduce the discharge of waste, and improve the recycling rate of the waste. 3.3. MFA-SD model simulation analysis and countermeasures 3.3.1. The simulation results This study selects biological substance consumption, fossil fuel consumption, building mineral consumption, TMI of 10,000 RMB of GDP, TMO of 10,000 RMB of GDP, industrial exhaust emissions and solid waste emissions in 2022, and then analyzes the simulation results of the eight indexes.(Fig. 8 should be below this paragraph) The nine scenarios can be analyzed by the changes of the eight indexes, as shown in Fig. 8. (1) Biological substance consumption: Scenario 2 and scenario 6 have the highest biological substance consumption, with 104.88 Mt and 104.80 Mt, respectively. Scenario 8 has the least biological substance consumption of only 88.39 Mt. (2) Fossil
3.3.2. Policy recommendations (1) This research acknowledged the important role of population control on the way of pursuing circular economy, which agreed with prior research that “have one fewer child” is considered the most 8
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effective way of achieving low carbon economy (Wynes and Nicholas, 2017). It is due to the increase in the population will generate a large amount of domestic waste and consume more resources. While controlling the rate of population growth, the government should also focus on raising people's awareness of green consumption, given the weak awareness of green consumption, such as the low ability of garbage classification and treatment, has long hindered the development of CE. (2) Guangdong Province should vigorously develop the tertiary industry and establish a support system for CE of the tertiary industry. Environmental health, logistic, finance and other industries in the tertiary industry are closely related to the development of CE. The cleaner production of the tertiary industry is an effective way to promote the development of CE in Guangdong Province. (3) The government should build a batch of CE demonstration cities and representative industrial parks. The CE industrial planning and key project construction of the demonstration cities and the industrial parks should be strengthened from the aspects of recycling of high efficiency of resource utilization, centralized pollution control, and popularization of clean production.
Guangdong in 2022. The results of MFA show that building mineral is the main component of DMI whose contribution increased rapidly from 42% in 2007 to 71% in 2010, given the development of the real estate industry. TMI dominantly contribute 76–81% to the hidden flow in the region. Among the components of DMO, gas output accounts for the largest proportion, reaching 76%. Among the components of TMO, the proportion of regional hidden flows is 87–91%. The results of scenario analysis show that, under the circumstance that the birth rate decreases by 2‰, the growth rate of the primary industry and the growth rate of the secondary industry both decrease by 2%, and the growth rate of the tertiary industry increases by 2%, the CE in Guangdong Province is the most favorable, with the least burden on the ecological environment. According to the content and results of scenario analysis, the following policy recommendations are proposed for the development of CE in Guangdong Province. (1) The government should control the rate of population growth while raising people's awareness of green consumption. (2) Guangdong Province should vigorously develop the tertiary industry and establish a support system for CE of the tertiary industry. (3) The government should build pilot demonstration cities and typical industrial parks for CE.
4. Conclusion
Acknowledgements
This study analyzes the material flow in Guangdong Province from 2007 to 2014. MFA and SD are combined to establish MFA-SD model to evaluate the development status of CE in Guangdong Province. Finally, nine scenarios are developed to examine the optimal mode of CE in
This work was supported by the Based Research Projects of National Natural Science Foundation of China (41871212), and the Based Research Projects of Northeastern University (N172504031).
Appendix (This part should be located after the References) See Table C1, Table D1, Table E1. Table A1 The intensity efficiency indexes of MFA. Index
Unit
Computational formula
DMI per capita TMI per capita DMO per capita TMO per capita The DMI of 10,000 RMB of GDP The TMI of 10,000 RMB of GDP The DMO of 10,000 RMB of GDP The TMO of 10,000 RMB of GDP
kg/ person kg/ person kg/ person kg/ person kg/10,000 RMB kg/10,000 RMB kg/10,000 RMB kg/10,000 RMB
DMI / population TMI / population DMO / population TMO / population DMI /GDP TMI /GDP DMO/GDP TMO/GDP
Table B1 Social economic subsystem equations and parameters. Variable
Unit
Type
Equation and parameters
Population Population growth Birth rate Population deaths Death rate Total GDP
Ten thousand people Ten thousand people／year ‰ Ten thousand people／year ‰ Billion RMB
Level variable Rate variable Constant (average) Rate variable Constant (average) Auxiliary variable
Per capita GDP
Billion RMB／Ten thousand people Billion RMB Billion RMB
Auxiliary variables
INTEG((Population growth - population deaths)(8156.05)) Population* birth rate 0.011285 Population* death rate 0.004541 Output value of primary industry + output value of secondary industry + output value of tertiary industry Total GDP／population
Level variable Rate variable
INTEG((Growth of output value of the primary industry)(1695.57)) Primary industry output value * Primary industry growth rate
% Billion RMB Billion RMB
Constant (average) Level variable Rate variable
0.0948 INTEG((Growth of output value of the secondary industry)(16004.6)) Secondary industry output value * Secondary industry growth rate
% Billion RMB Billion RMB
Constant (average) Level variable Rate variable
0.1024 INTEG((Growth of output value of the tertiary industry)(14076.8)) Tertiary industry output value * tertiary industry growth rate
%
Constant (average)
0.1309
Primary industry output value Growth of output value of the primary industry Primary industry growth rate Secondary industry output value Growth of output value of the secondary industry Secondary industry growth rate Tertiary industry output value Growth of output value of the tertiary industry Tertiary industry growth rate
9
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Table C1 Environmental Impact Subsystem Equations and Parameters. Variable
Unit
Type
Equation and parameters
Industrial exhaust emissions
Ten thousand tons Ten thousand tons %
Level variable
INTEG((Industrial exhaust growth)(13603.4))
Rate variable
Secondary industry output value *100 million RMB industrial exhaust production rate
Lookup function Level variable
WITHLOOKUP((Time)([(2007,-1)-(2022,1)],(2007,0.0120815),(2008,0.052323),(2009,0.117859), (2010,0.0608235),(2011,-0.00323594),(2012,0.022271),(2013,0.0182609),(2022,0.0400548))) INTEG((Solid waste growth)(3852.4))
Rate variable
Level variable
Secondary industry output value * billion RMB solid waste production rate WITHLOOKUP((Time)([(2007,-1)-(2022,1)],(2007,0.0612698),(2008,-0.00498319), (2009,0.0369836),(2010,0.017216),(2011,0.00446086),(2012,-0.00196774),(2013,-0.00850894), (2022,0.0149244))) INTEG((COD growth)(101.7))
Rate variable
Secondary industry output value * Billion RMB COD production rate
Lookup function
WITHLOOKUP((Time)([(2007,-1)-(2022,2)],(2007,-0.000333654),(2008,-0.00028321),(2009,0.000273034),(2010,0.00449615),(2011,-0.000312452),(2012,-0.000253309),(2013,0.000218319),(2022,0.0004032)))
Industrial exhaust growth Billion RMB industrial exhaust production rate Solid waste emissions Solid waste growth Billion RMB solid waste production rate COD emissions
Ten thousand tons Ten thousand tons %
Lookup function
Ten thousand tons Ten thousand tons %
COD growth Billion RMB COD production rate
Table D1 Resource Consumption Subsystem Equations and Parameters. Variable
Unit
Type
Equation and parameters
Biological substance Biological substance growth Biological substance growth rate
Ten thousand tons Ten thousand tons %
Level variable Rate variable Lookup function
Fossil fuels Fossil fuels growth Fossil fuels growth rate
Ten thousand tons Ten thousand tons %
Level variable Rate variable Lookup function
Building minerals Building minerals growth Building minerals growth rate
Ten thousand tons Ten thousand tons %
Level variable Rate variable Lookup function
Hidden flow total
Ten thousand tons
Regional hidden flow Regional hidden flow growth Regional hidden flow growth rate Import hidden flow Import hidden flow growth Import hidden flow growth rate Export hidden flow Export hidden flow growth Export hidden flow growth rate
Ten thousand tons Ten thousand tons %
Auxiliary variables Level variable Rate variable Lookup function
INTEG((Biological substance growth)(4448.32)) Biological substance * biological substance growth rate WITHLOOKUP((Time)([(2007,-0.2)-(2022,0.3)],(2007,0.0618705),(2008,-0.0265691), (2009,0.0558281),(2010,0.0964398),(2011,0.0338028),(2012,0.0557619),(2013,0.0539304), (2022,0.06))) INTEG((Fossil fuels growth)(3923.53)) Fossil fuels * fossil fuels growth rate WITHLOOKUP((Time)([(2007,-0.2)-(2022,0.3)],(2007,0.125158),(2008,-0.0051511), (2009,0.199697),(2010,-0.0801174),(2011,0.049953),(2012,0.0647727),(2013,0.0324943), (2022,0.1))) INTEG((Building minerals growth)(10046.1)) Building minerals * building minerals growth rate WITHLOOKUP((Time)([(2007,-0.5)-(2022,3)],(2007,0.0437971),(2008,2.39857),(2009,0.0898571), (2010,-0.299325),(2011,0.00722233),(2012,0.124061),(2013,0.0676104),(2022,0.455186))) Export hidden flow + regional hidden flow + import hidden flow
Ten thousand tons Ten thousand tons %
Level variable Rate variable Lookup function
Ten thousand tons Ten thousand tons %
Level variable Rate variable Lookup function
INTEG((Regional hidden flow growth)(148715)) Regional hidden flow * regional hidden flow growth rate WITHLOOKUP((Time)([(2007,-1)-(2022,1)],(2007,0.129311),(2008,0.411375),(2009,0.187997), (2010,-0.121941),(2011,0.0537355),(2012,0.13136),(2013,0.054019),(2022,0.1613))) INTEG((Import hidden flow growth)(22744.6)) Import hidden flow * import hidden flow growth rate WITHLOOKUP((Time)([(2007,-1)-(2022,1)],(2007,0.0377039),(2008,0.0308238),(2009,-0.124777), (2010,-0.11091),(2011,-0.0116003),(2012,0.13532),(2013,-0.0655004),(2022,-0.0155629))) INTEG((Export hidden flow growth)(4203.58)) Export hidden flow * export hidden flow growth rate WITHLOOKUP((Time)([(2007,-1)-(2022,1)],(2007,0.975685),(2008,0.270643),(2009,-0.176216), (2010,0.0547354),(2011,-0.295468),(2012,0.005011),(2013,-0.224004),(2022,0.0871981)))
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Table E1 Intensity Efficiency Subsystem Equations and Parameters. Variable
Unit
Type
Equation and parameters
Import Import growth Import growth rate
Ten thousand tons Ten thousand tons %
Level variable Rate variable Lookup function
Export Export growth Export growth rate
Ten thousand tons Ten thousand tons %
Level variable Rate variable Lookup function
DMI
Ten thousand tons
TMI
Ten thousand tons
DMO
Ten thousand tons
TMO
Ten thousand tons
DMI of 10,000 RMB of GDP TMI of 10,000 RMB of GDP DMO of 10,000 RMB of GDP TMO of 10,000 RMB of GDP DMI per capita
Tons RMB Tons RMB Tons RMB Tons RMB Tons
TMI per capita
Tons / person
DMO per capita
Tons / person
TMO per capita
Tons / person
Auxiliary variables Auxiliary variables Auxiliary variables Auxiliary variables Auxiliary variables Auxiliary variables Auxiliary variables Auxiliary variables Auxiliary variables Auxiliary variables Auxiliary variables Auxiliary variables
INTEG((Import growth)(5755.94)) Import * import growth rate WITHLOOKUP((Time)([(2007,-1)-(2022,1)],(2007,-0.051135),(2008,0.0767246),(2009,-0.046568), (2010,-0.0364415),(2011,0.0749748),(2012,0.0909905),(2013,0.193661),(2022,0.0431724))) INTEG((Export growth)(596.1)) Export * export growth rate WITHLOOKUP((Time)([(2007,-1)-(2022,1)],(2007,0.41384),(2008,0.164205),(2009,-0.125257), (2010,0.0329729),(2011,-0.198279),(2012,0.00538837),(2013,-0.155201),(2022,0.0196669))) Fossil fuels + building minerals + Biological substances + imports
/ ten thousand / ten thousand / ten thousand / ten thousand / person
DMI + regional hidden flow + import hidden flow Exports + solid waste emissions + industrial exhaust emissions Export hidden flow + regional hidden flow + DMO DMI / total GDP TMI / total GDP DMO / total GDP TMO / total GDP DMI /population TMI / population DMO / population TMO / population
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Full length article
Pathways towards regional circular economy evaluated using material flow analysis and system dynamics Chengkang Gaoa,*, Chengbo Gaoa, Kaihui Songb, Kejing Fangc,* a
SEP Key Laboratory of Eco-Industry, Northeastern University, Shenyang, Liaoning, 110819, China Department of Geographical Science, University of Maryland, College Park, 20742, MD, USA c Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou, 510640, China b
ARTICLE INFO
ABSTRACT
Keywords: Circular economy MFA System dynamics Coupling model Evaluation indexes system
Circular Economy (CE) offers insights to sustainable production and consumption by integrating environmental analysis to the socioeconomic system. To promote sustainable development in Guangdong Province, this study integrates System Dynamics (SD) and Material Flow Analysis (MFA) into CE theories to establish a framework to comprehensively evaluate regional economies. Nine development scenarios are further developed to provide strategic recommendations for the development of CE in Guangdong. The results of these scenarios show that the CE of Guangdong is most effective among the nine scenarios when the birth rate is reduced by about 2‰, the growth rates of the primary and secondary industries are reduced by 2%, and the growth rate of the tertiary industry increased by 2%. The biological substance consumption, fossil fuel consumption, building mineral consumption, industrial exhaust emissions and solid waste emissions are 88.39 Mt, 86.63 Mt, 108.16 Mt, 280.90 Mt, and 69.02 Mt respectively. The total material input (TMI) of 10,000 RMB of GDP and the total material output (TMO) of 10,000 RMB of GDP are 49.64 kt/10,000 RMB and 42.70 kt/10,000 RMB in 2022 respectively. Based on the status quo and simulated results, this study acknowledges the importance of population control and highlights the vigorous development of tertiary industry in economic construction. Policy interventions such as building pilot demonstrative smart cities and industrial parks would facilitate long-term sustainability of urban systems.
1. Introduction As early as the end of the 1960s, American economist Boulding first proposed the Spaceship Earth theory, which led to the emergence of CE (Boulding, 1966). Since then, the development of CE has undergone three stages (Zhu, 2017). The first stage was a preliminary exploration stage, from 1966 to 1992, when some ideas and concepts of CE were proposed, emphasizing the importance of environmental sustainability of economic development (Commoner et al., 1997; Pearce and Turner, 1990). Meadows et al. (1992, p.1204) believed that human beings would eventually move toward CE development through unremitting efforts. The second stage was from 1992 to 2010 when increased theoretical model divergent research came on stage. Researchers began to think about how to switch from linear economy mode to a dynamic closed-looped system from an operational sense, and put forward a new CE mode with
various socio-environmental implications, intending to achieve green development from production and consumption. During this period, some research explored the internal relationship between the ecological environment and the development of CE at different levels (Mo et al., 2009; Wen et al., 2007; Gibbs, 2010; Mirata, 2004). In Germany, the CE concept was introduced into environmental policy to address issues related to the consumption of raw materials and natural resources (Winans et al., 2017). Andersen (2007, p.133) analyzed environmental policy priorities through external environmental pricing and provided theoretical support for achieving sustainable development of the CE. CE had phased into the third stage since 2010 with a flourishment of theoretical integration and application. Some scholars and entrepreneurs made joint efforts to apply the concepts of CE to enterprise development, promoting the application of CE at the enterprise level (de Oliveira et al., 2018; Sousa-Zomer et al., 2018). Singh et al. (2018, p.313) studied the readiness and acceptance of CE for micro, small and
Abbreviation: CE, circular economy; SD, system dynamics; MFA, material flow analysis; PCA, principal component analysis; AHP, analytic hierarchy process; DMI, direct material input; TMI, total material input; HF(IF), hidden flow (indirect flow); DMO, direct material output; HF(OF), hidden flow (indirect flow); TMO, total material output; COD, chemical oxygen demand ⁎ Corresponding authors. E-mail addresses: [email protected] (C. Gao), [email protected] (K. Fang). https://doi.org/10.1016/j.resconrec.2019.104527 Received 22 October 2018; Received in revised form 28 September 2019; Accepted 29 September 2019 0921-3449/ © 2019 Elsevier B.V. All rights reserved.
Please cite this article as: Chengkang Gao, et al., Resources, Conservation & Recycling, https://doi.org/10.1016/j.resconrec.2019.104527
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medium-sized enterprises. Popa and Popa (2017) proposed a framework for startup businesses to apply the theory of CE innovation. Meanwhile, the number of related research articles on CE had increased sharply (Ghisellini et al., 2016; Lieder and Rashid, 2016; Martins, 2018; Vladimirova, 2017; Saidani et al., 2018; Laso et al., 2018). In 2016, Nature published a series of special articles on CE when Boulding proposed this concept for 50 years (Stahel, 2016). The CE theory was introduced into China in 1977 and attracted the attention of the Chinese government in the late 1990s (Xue et al., 2010). Since the 20th century, the Chinese government had promulgated a series of CE policies, which triggered the development of CE at three levels – enterprises, regional and national levels (Li et al., 2010; Mathews and Tan, 2011). In 2008, China became the first country to adopt a Circular Economy Promotion Law (Winans et al., 2017). The practice of CE was also widely promoted and advanced by modern enterprises. Since 2001, the eco-industrial parks and CE demonstration parks had been deployed by the Chinese government coupled with continuous efforts of innovative businesses. In 2005, China launched the first batch of CE pilot projects. At the end of 2007, the second batch of national CE pilot projects was promoted, as strong support and significant extension of the practice of CE (He, 2009). Intensive research focused on advancing the rigor and flexibility of models used for CE as modern complexity being added to the humanenvironmental system. By the beginning of the 21st century, various perspectives and insights enriched research of the circular economy. Some scholars had improved CE index system based on MFA and SD (Moriguchi, 2007; Nakamura and Kondo, 2018; Jacobi et al., 2018). Geng et al. (2013, p.1526) proposed to complete and improve the CE indicator system by including socio-economic and demographic indicators, coupled with environmental accounting tools such as MFA and lifecycle assessment (LCA), and include, to comprehensively assess the regional circular economy. The practice of CE evaluation also highlighted the incorporation of decision-making approaches such as PCA (Yang et al., 2011) and AHP (Zhou et al., 2010) to inform the policymaking process. Zhang et al. (2017, p.238) used three-stage data envelopment analysis to study the interaction between dynamic industrial eco-efficiency evaluations and economic development in 30 provinces, in order to bridge relevant knowledge gaps. Jiang (2011, p.125) used fuzzy comprehensive method to evaluate the circular economy development level in Jiangsu, Heilongjiang and Qinghai, which provided an analytical framework as well as an empirital analysis of regional evaluation. The nationwide advocacy on sustainable development drove the flourish of CE in China. However, solving the dynamic real-world CE problems increasingly requires a widely acknowledged framework and integrated models to accommodate dynamic yet concrete data inputs, meanwhile, the incorporation of socio-economic and demographic indicators into the system. To this end, this study proposed a framework to quantify and comprehensively evaluate the implementation and effectiveness of regional CE by the combination of MFA and SD approach. The regional analytical framework is further applied to Guangdong Province as a case study. The comprehensive analytical framework of regional CE developed in this study could provide references for future research and development strategies to achieve long-term sustainable production and consumption.
flow of “resource-product-pollution emission” to a closed-loop process of “resource-product-renewable resources”. (2) System Dynamics is an interdisciplinary branch of systematical analysis and management science that studies the behavior of a system through system feedback. SD can also be used to analyze complex system problems that include societal, economic and ecological spheres (Hjorth and Bagheri, 2006). The overarching equation of SD is as follows. (1)
X = f (X , U , T )
where X Rm ； U Rr ; R is Euclidean space; m and r represent the dimensions of Euclidean space. SD can provide a transparent and integrative model that helps people understand the internal structure of the system, capture the behavior of the system, and effectively simulate the actual system to obtain the most scientific and appropriate decision scheme. (3) Material Flow Analysis is proposed on the bases of industrial metabolism theory and social metabolism theory. It is widely used to analyze the amount and direction of the material input and output in a certain region. MFA follows the law of conservation of mass and can describe the material input and output paths of the relevant system. MFA serves as not only the link between the human-social-economic system and ecological environment, but also a significant approach to regulate and control the coordinated development of economy and ecological environment to pursue sustainable development. The quantification and interpretation of indexes offer important tools in the process of MFA. Through the analysis of indexes, policymakers can understand the relationship between economic and environmental development within a country or region, and thus develop scientific and rational management policies. The region is a relatively open system, in which the material flow not only exchange with the outside world, but also can be self-contained to some extent. Therefore, the input account of the region contains internal direct input accounts, import input, and hidden flows. The output account of the region contains direct output accounts and export material flow, as well as export hidden flows. For comprehensive consideration, the following MFA evaluation indexes are selected, as shown in Table 1. The intensity efficiency indexes of MFA are mainly expressed by the indexes of material consumption intensity, which are used to measure the resource consumption per capita or the resource consumption per 10,000 RMB of GDP in the economic system. The intensity efficiency indexes of MFA are mainly affected by the total economic volume, population base, and industrial structure. To better reflect the input and output intensity, the following indexes are determined to characterize the input and output intensity efficiency of material flows: DMI per capita, TMI per capita, DMO per capita, TMO per capita, DMI of 10,000 RMB of GDP, TMI of 10,000 RMB of GDP, DMO of 10,000 RMB of GDP, and TMO of 10,000 RMB of GDP.
Table 1 Evaluation indexes and calculation method of MFA.
2. Theory and method 2.1. The related method of constructing MFA-SD model (1) Circular Economy is an emerging economic pattern and social development model, which centers on environmental protection, the full reuse of resources and the recycling of waste. CE takes “Reduce, Reuse, Recycle” as the yardstick, features with low consumption, low emission, and high efficiency of economic development (Kirchherr et al., 2017; Reike et al., 2018). It promotes the transformation from traditional linear economic development characterized by a one-way
Index classification
Index
Computational formula
Input
DMI TMI HF(IF)
Output
DMO
DMI = regional mining + regional imports TMI = DMI + HF(IF) HF(IF) = regional hidden flow + import hidden flow DMO = gas output + solid waste + regional exports HF(OF) = regional hidden flow + export hidden flow TMO = DMO + HF(OF)
HF(OF) TMO
2
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Fig. 1. Coupling of MFA-SD.
subsystem. Conversely, the socioeconomic subsystem and the intensity efficiency subsystem have a negative feedback relationship. In the same way, we can get the feedback relationship on the rest of the loop.
2.2. MFA-SD model description The coupling of MFA and SD model can provide a research framework to effectively analyze the human-environmental interaction and its sustainability, facilitating policy formulation which would be suitable for regional CE development.
2.2.3. Construction of total stock-flow diagram of MFA-SD model system The MFA-SD model combines the intensity efficiency indexes of the input and output in material flow accounting, indexes of MFA, and regional CE evaluation indexes in the material flow accounting. These indexes are integrated into each subsystem. The four subsystems are organically combined to form a relatively complete simulation model of CE, as shown in Fig. 3. The output of the secondary industry in the socioeconomic subsystem drives the industrial exhaust and solid waste. The subsystem is thus connected with the growth of industrial exhaust, solid waste and COD in the environmental impact subsystem through the output variables of the secondary industry. In the same way, the intensity efficiency subsystem is associated with the resource consumption subsystem through variables such as biological substance consumption and fossil fuel consumption. The environmental impact subsystem is connected with the intensity efficiency subsystem through industrial exhaust emissions, solid waste emissions, and regional exports. The socioeconomic subsystem is related to the intensity efficiency subsystem through variables such as population and GDP. Therefore, the four subsystems are interconnected through internal relationships. The flow chart of the system consists of 15 level variables, 16 rate variables, 5 constant variables, 14 auxiliary variables, and 11 lookup functions, described by 61 mathematical equations. The stock-flow diagram of the system is shown in Fig. 3, and the equations and parameters of the four subsystems are shown in Appendix Table B1, Table C1, Table D1, and Table E1.
2.2.1. Theory of MFA-SD model The MFA method can clearly reflect the connotation of CE that allows a detailed description of the amount and direction of materials consumed in the conceptual system, instead of a black box. SD contributes to revealing the cause-effect relationships by scrutinizing the internal structure of the system. The MFA-SD coupling model is shown in Fig. 1. The MFA-SD coupling model uses SD to connect thirteen indexes of MFA to form a regional CE evaluation model. The thirteen indexes are DMI, TMI, HF, DMO, TMO, DMI per capita, TMI per capita, DMO per capita, TMO per capita, DMI of 10,000 RMB of GDP, TMI of 10,000 RMB of GDP, DMO of 10,000 RMB of GDP, and TMO of 10,000 RMB of GDP (as shown in Appendix Table A1). 2.2.2. Construction of MFA-SD model According to the material flow evaluation indexes and the characteristics of CE, this study selects 20 indexes for the comprehensive evaluation of CE in Guangdong Province. Afterward, these indexes are linked by the SD theory and assigned to four subsystems. The four subsystems are as follows: socioeconomic subsystem, resource consumption subsystem, intensity efficiency subsystem, environmental impact subsystem. The socioeconomic subsystem consists of birth rate, death rate, and the growth rates of primary, secondary, and tertiary industry. Resource consumption subsystem includes fossil fuel consumption, biological substance consumption, building mineral consumption and hidden flows such as indirect effects from regional transactions and imports. Intensity efficiency subsystem includes the DMI of 10,000 RMB of GDP, TMI of 10,000 RMB of GDP, DMO of 10,000 RMB of GDP, TMO of 10,000 RMB of GDP, DMI per capita, TMI per capita, DMO per capita and TMO per capita. Environmental impact subsystem includes industrial exhaust emissions, solid waste emissions, and COD emissions. We take 2007 as the base year for its relatively complete data and run a 15-year model. According to the rule of thumb, the time step size selected in this paper is one year, as shown in Fig. 2. Fig. 2 presents positive and negative relationships between the four subsystems and the regional CE. The growth of the three major industries in the socioeconomic system will contribute to the development of regional CE, but at the same time will increase the emissions of various substances in the environmental impact subsystem; accordingly, the indexes in the intensity efficiency subsystem will decrease with the population grows. Therefore, there is a positive feedback relationship among the socioeconomic subsystem, regional CE and environmental impact
2.3. Data collection and processing 2.3.1. Data source This paper finds information on the annual consumption of various substances such as biomass consumption, building mineral consumption, etc. through the Guangdong Statistical Yearbook, China Statistical Yearbook, China Industrial Economics Statistical Yearbook, and China Energy Statistics Yearbook. The information is processed according to relevant research. 2.3.2. Processing method (1) The regional material flow accounting system is based on the EU guidelines (2001, p.9) and is consistent with the MFA method of Eurostat in light of regional realities. (2) For locally mined and produced materials, only primary raw materials are counted (secondary products excluded), to avoid 3
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Fig. 2. Causality of development of CE with MFA-SD model.
omission and double counting. (3) Import and export commodities that are not measured by weight in the yearbook shall be converted into weight units for consistency. (4) The production of aquatic and livestock products that are cultivated and fed with agricultural products shall be treated as material stocks.
the gas input, R is the regional imports, B is the biological substance production, and N is the non-biological substance production. Biological substances mainly include agriculture, forestry, animal husbandry, and fishery. The animal husbandry sector is represented by flows instead of stock due to data limitation. The fishery is measured by wild-caught fish, while farmed fish fed with agricultural products are not counted. Non-biological substances refer to fossil fuels and building minerals. Fossil fuels mainly include four major categories: raw coal, crude oil, natural gas, and electric power. The building minerals mainly include bricks, tiles, stones, sand, and boulder; the total weight of building minerals can be calculated by the annual completion area of the building. Air inputs include oxygen consumption of fuel combustion, oxygen consumption of human and biological respiration. The photosynthesis of plants in the natural environment, the O2 required for the biology and soil respiration and the CO2 emissions are not directly involved in human economic activities, thus not being counted. The oxygen consumption of fuel combustion and the oxygen consumption of human and biological respiration need to be estimated.
2.4. Methods of material flow accounting 2.4.1. Accounting of material flow input The input of the region includes three parts: regional mining, air input, and regional imports. The regional mining mainly refers to the biological substance production and non-biological substance production.
I = Mj + Gin + R
(2)
Mj = B + N
(3)
where I represents total input in a region, Mj is the regional mining, Gin is
Fig. 3. Total stock-flow diagram of regional CE based on MFA-SD model. 4
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Fig. 4. Parameters of nine scenarios.
In this study, the amount of O2 consumed in fuel combustion (Q) was calculated by the following formula (Yang, 2013).
Q = S × 0.5 + C × 0.75
that is harvested from the crops not involving commodity economic activities, mainly represented by crop straw. In this study, the amount of crop straw unutilized is used to estimate the amount of crop residues. The hidden flow of fossil fuels mainly refers to the ecological burden caused by the exploitation of raw coal, crude oil and natural gas, which directly or indirectly affects the environment. The import and export hidden flow can be calculated by multiplying the main commodity of the import and export by the corresponding hidden flow coefficient. The topsoil movement caused by the infrastructure construction mainly includes the land taken from houses excavation, transportation, and water conservancy facilities. The hidden flow of infrastructure construction is calculated by the amount of excavation of building construction; moreover, the amount of excavation of the building is estimated by the following formula (Zhou et al., 2006).
(4)
where S is the emission of SO2 , C is the emission of CO2 . The estimation of annual oxygen consumption by human-beings is based on an averaged respiration rate of 7–8 liters of air per minute during rest, while those consumed by livestock is based on the year-end output, mainly including cattle, pigs, sheep and, poultry. Imported substances outside the region mainly include biological substances, non-biological substances, minerals, industrial raw materials, consumables, finished products, and semi-finished products. 2.4.2. Accounting of material flow output The output of the region includes solid waste, gas output, and regional export.
O = W + Gout + E
=
(5)
2.5. The design of model simulation scenarios Nine scenarios are developed with various conditions to provide options for development in seek of strategic and flexible policy to promote CE in Guangdong Province. An optimal scenario among the nine is also obtained that describes an ideal yet relatively feasible development mode This study selects the birth rate, the growth rate of the primary industry, the growth rate of the secondary industry and the growth rate of the tertiary industry as the control variables. Four parameters varied in different scenarios (as shown in Fig. 4) with simulation to 2022. The sum of growth rates of the three major industries is consistent with the projected growth of GDP, but the share of each industry varies accordingly. Due to the rapid development of the tertiary industry, the growth rate of the output value of the tertiary industry will be increased, and the growth rate of the output value of the primary and second industries will be lowered. For the stable and healthy development of CE, the adjustment scope of each variable should not be too large. After comprehensive consideration, each variable is adjusted by two units according to its original unit.
2.4.3. Accounting of hidden flow Hidden flows mainly consist of regional hidden flow, import, and export hidden flows. Regional hidden flows ( ) are the sum of flow of agricultural residues, topsoil movement of infrastructure construction and fossil fuel consumption. Import and export hidden flows refer to hidden flows caused by import and export commodities, such as the total amount of material consumed during the mining or transportation of imported and exported metals and non-metals.
+
+
+
(7)
In the equation, represents the regional amount of excavation of the building construction and is the construction completion area of the year.
where O represents the total output of the region, W is the solid waste, Gout is the gas output, and E is the regional export. Solid waste mainly includes industrial solid waste and domestic garbage. The gas output mainly includes sulfur dioxide, carbon dioxide, nitrogen oxides, and chimney, in which carbon dioxide only counts emissions from fossil fuel combustion. Export substances mainly include biological substances, non-biological substances, minerals, industrial raw materials, energy raw materials, consumables, finished products, and semi-finished products.
=
× 3.2 × 1.55
(6)
where is the total hidden flow, α is the biological substance hidden flow, is the infrastructure construction hidden flow, is the fossil fuel hidden flow, and is the import and export hidden flow. The hidden flow of biological substance mainly refers to the waste 5
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Fig. 5. Material flow change in Guangdong Province during 2007–2014. (a): input, (b): output, (c): hidden flow.
3. Results and discussion
trends exist among the biological substance hidden flows, the import hidden flows, and the export hidden flows. The total hidden flow of fossil fuel was 29–38 times as large as its corresponding consumption because of the large coefficient of the fossil fuel hidden flow. As the consumption of building minerals in the input increased from 104.86 Mt in 2008 to 356.38 Mt in 2009, the corresponding hidden flow increased 3.4 times, from 273.45 Mt to 92.94 Mt.
3.1. Analysis of accounting results 3.1.1. Analysis of the input, output and total hidden flows of material flow Fig. 5(a) shows the changes in input. It can be seen that the biological substance, fossil fuels, and outsourcings are generally in increasing trajectories. The air input had slowly increased from 123.33 Mt in 2007 to 165.09 Mt in 2014, with an average annual growth rate being about 4%. The financial crisis slightly negatively affected construction industry when it broke out, but real estate companies made use it as a business opportunity given the cultural factors and policy interventions. This led to the dramatic increase in construction during 2008-2010. The net flow of infrastructure started to decrease sharply during 2009–2010 due to regulation from the central government. Fig. 5(b) shows the changes in output. The regional export increased from 5.96 Mt in 2007 to 9.81 Mt in 2009 and, afterward, decreased to 6.04 Mt in 2014. Solid waste generally showed an upward trend between 2007 and 2012, with an average annual growth rate of 9%. It declined slightly in 2009 and then stabilized. The gas output was gradually increasing, with an average annual growth rate of 5%, and the growth rate from 2010 to 2014 was slower than that of 2007–2009. Fig. 5(c) shows the changes in hidden flows. Similar increasing
3.1.2. Analysis of material flow evaluation indexes Fig. 6(a) shows that DMI of Guangdong Province increased from 241.74 Mt in 2007 to 521.75 Mt in 2014, with an average annual growth rate of 12%. Building materials are the major contributor to DMI, representing 42% of the DMI in 2007 to 71% in 2010. The rapid development of real estate in China resulted in a sharp increase in building minerals demand.(Fig. 6 should be at the top of this paragraph) Fig. 6(b) indicates that the regional hidden flow is a dominant component in the TMI, representing 76–81% of the TMI during the study period. It could be explained by the large coefficient of fossil fuel consumption in the hidden flow, which means that fossil fuels mining would bring a huge ecological burden to the natural environment. Fig. 6(c) and (d) show that the gas output is the major contributor to DMO, reaching a maximum of 76% of the total, while the regional hidden flows contributed 87–91% to TMO during the study period. 6
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Fig. 6. The changes in DMI, TMI, DMO, and TMO based on MFA in Guangdong, China. (a): DMI, (b): TMI, (c): DMO, (D): TMO.
mining will lower the gas output and the hidden flow in the TMI and TMO thus plays an important role in the development of local CE. 3.2. Comprehensive evaluation of CE in Guangdong Province The comprehensive evaluation of regional CE is helpful to examine the problems existing in the development of regional social economy, to adopt effective regulatory measures to make the CE gradually develop towards the direction of high quality and high efficiency. According to the evaluation index of each subsystem, the overall development of CE is analyzed.(Fig. 7 should be below this paragraph) It can be seen from Fig. 7 that the index of socioeconomic subsystem showed wavy changes, mainly affected by the economic crisis from 2008 to 2009, decreased from 0.241 in 2008 to 0.074 in 2009. The index of the resource consumption subsystem showed a decreasing trend. With the development of society and economic growth, the demand for resources increased year by year, leading to the decline of the index of the resource consumption subsystem. The index of intensity efficiency subsystem increased from 0.134 in 2007 to 0.382 in 2014, indicating that the resource and environmental cost of economic development decreased over time, which promoted the development of
Fig. 7. Comprehensive evaluation index of CE in Guangdong Province.
In summary, attention should be attached to the theoretical basis and essential requirements of the CE, especially reducing the consumption of fossil fuels by reducing mining activities. The reduction of 7
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Fig. 8. The change of indexes in nine scenarios in 2022.
fuel consumption: Scenario 1 consumes the most fossil fuels, reaching 109.00 Mt, and scenario 8 is the least, only 86.63 Mt. (3) Building mineral consumption: Scenario 8 consumes the least amount of 1081.56 Mt, and scenario 2 consumes 3.74 times of scenario 8, up to 4043.97 Mt. (4) TMI of 10,000 RMB of GDP: Scenario 3 is the highest, with 69.97 kt/10,000 RMB, and scenario 5 is the lowest, at 48.90 kt/ 10,000 RMB. (5) TMO of 10,000 RMB of GDP: The TMO of 10,000 RMB of GDP in scenario 3 is 45.72 kt/10,000 RMB, the highest among the nine scenarios, and scenario 5 is the lowest, only 41.84 kt/10,000 RMB. (6) Industrial exhaust emissions: Scenario 8 has the lowest emissions of only 280.90 Mt, and scenario 1 is 1.12 times that of scenario 8, which is 340.89 Mt. (7) Solid waste emissions: Scenario 3 generates the highest (78.99 Mt) solid wastes while scenario 8 generates the lowest (69.02 Mt).(Fig. 8 should be at the top of this paragraph) Considering the impact of the above eight indexes on different scenarios, only the total GDP is positively related to the CE, and the rest are negatively associated with the CE. The optimal scenario is featured with high total GDP and low other indexes. After comparison, scenario 8 is the most conducive to the development of CE. That is, when the birth rate is reduced by 2‰, the growth rate of the primary industry and the secondary industry are both reduced by 2%, and the growth rate of the tertiary industry is increased by 2%, which could best lead to CE.
CE. The index of the environmental impact subsystem decreased significantly first and then rose slightly. Based on the impact of the four subsystems on the comprehensive evaluation index, the comprehensive evaluation index of CE in Guangdong Province fluctuated greatly, which increased from 0.649 in 2007 to 0.691 in 2008, dropped down to 0.420 in 2009, reached a plateau around 0.544 in 2011; afterwards, showed a steady increase from 2012 (0.471) to 2014 (0.495).(Fig. 7 should be at the top of this paragraph) To sum up, the main problems in the current CE in Guangdong Province are the excessive consumption of resources, increasing emissions of pollutants, and the unstable development of CE. Therefore, the key to the development of CE in Guangdong Province is to control the consumption of resources, improve the intensity efficiency, reduce the discharge of waste, and improve the recycling rate of the waste. 3.3. MFA-SD model simulation analysis and countermeasures 3.3.1. The simulation results This study selects biological substance consumption, fossil fuel consumption, building mineral consumption, TMI of 10,000 RMB of GDP, TMO of 10,000 RMB of GDP, industrial exhaust emissions and solid waste emissions in 2022, and then analyzes the simulation results of the eight indexes.(Fig. 8 should be below this paragraph) The nine scenarios can be analyzed by the changes of the eight indexes, as shown in Fig. 8. (1) Biological substance consumption: Scenario 2 and scenario 6 have the highest biological substance consumption, with 104.88 Mt and 104.80 Mt, respectively. Scenario 8 has the least biological substance consumption of only 88.39 Mt. (2) Fossil
3.3.2. Policy recommendations (1) This research acknowledged the important role of population control on the way of pursuing circular economy, which agreed with prior research that “have one fewer child” is considered the most 8
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effective way of achieving low carbon economy (Wynes and Nicholas, 2017). It is due to the increase in the population will generate a large amount of domestic waste and consume more resources. While controlling the rate of population growth, the government should also focus on raising people's awareness of green consumption, given the weak awareness of green consumption, such as the low ability of garbage classification and treatment, has long hindered the development of CE. (2) Guangdong Province should vigorously develop the tertiary industry and establish a support system for CE of the tertiary industry. Environmental health, logistic, finance and other industries in the tertiary industry are closely related to the development of CE. The cleaner production of the tertiary industry is an effective way to promote the development of CE in Guangdong Province. (3) The government should build a batch of CE demonstration cities and representative industrial parks. The CE industrial planning and key project construction of the demonstration cities and the industrial parks should be strengthened from the aspects of recycling of high efficiency of resource utilization, centralized pollution control, and popularization of clean production.
Guangdong in 2022. The results of MFA show that building mineral is the main component of DMI whose contribution increased rapidly from 42% in 2007 to 71% in 2010, given the development of the real estate industry. TMI dominantly contribute 76–81% to the hidden flow in the region. Among the components of DMO, gas output accounts for the largest proportion, reaching 76%. Among the components of TMO, the proportion of regional hidden flows is 87–91%. The results of scenario analysis show that, under the circumstance that the birth rate decreases by 2‰, the growth rate of the primary industry and the growth rate of the secondary industry both decrease by 2%, and the growth rate of the tertiary industry increases by 2%, the CE in Guangdong Province is the most favorable, with the least burden on the ecological environment. According to the content and results of scenario analysis, the following policy recommendations are proposed for the development of CE in Guangdong Province. (1) The government should control the rate of population growth while raising people's awareness of green consumption. (2) Guangdong Province should vigorously develop the tertiary industry and establish a support system for CE of the tertiary industry. (3) The government should build pilot demonstration cities and typical industrial parks for CE.
4. Conclusion
Acknowledgements
This study analyzes the material flow in Guangdong Province from 2007 to 2014. MFA and SD are combined to establish MFA-SD model to evaluate the development status of CE in Guangdong Province. Finally, nine scenarios are developed to examine the optimal mode of CE in
This work was supported by the Based Research Projects of National Natural Science Foundation of China (41871212), and the Based Research Projects of Northeastern University (N172504031).
Appendix (This part should be located after the References) See Table C1, Table D1, Table E1. Table A1 The intensity efficiency indexes of MFA. Index
Unit
Computational formula
DMI per capita TMI per capita DMO per capita TMO per capita The DMI of 10,000 RMB of GDP The TMI of 10,000 RMB of GDP The DMO of 10,000 RMB of GDP The TMO of 10,000 RMB of GDP
kg/ person kg/ person kg/ person kg/ person kg/10,000 RMB kg/10,000 RMB kg/10,000 RMB kg/10,000 RMB
DMI / population TMI / population DMO / population TMO / population DMI /GDP TMI /GDP DMO/GDP TMO/GDP
Table B1 Social economic subsystem equations and parameters. Variable
Unit
Type
Equation and parameters
Population Population growth Birth rate Population deaths Death rate Total GDP
Ten thousand people Ten thousand people／year ‰ Ten thousand people／year ‰ Billion RMB
Level variable Rate variable Constant (average) Rate variable Constant (average) Auxiliary variable
Per capita GDP
Billion RMB／Ten thousand people Billion RMB Billion RMB
Auxiliary variables
INTEG((Population growth - population deaths)(8156.05)) Population* birth rate 0.011285 Population* death rate 0.004541 Output value of primary industry + output value of secondary industry + output value of tertiary industry Total GDP／population
Level variable Rate variable
INTEG((Growth of output value of the primary industry)(1695.57)) Primary industry output value * Primary industry growth rate
% Billion RMB Billion RMB
Constant (average) Level variable Rate variable
0.0948 INTEG((Growth of output value of the secondary industry)(16004.6)) Secondary industry output value * Secondary industry growth rate
% Billion RMB Billion RMB
Constant (average) Level variable Rate variable
0.1024 INTEG((Growth of output value of the tertiary industry)(14076.8)) Tertiary industry output value * tertiary industry growth rate
%
Constant (average)
0.1309
Primary industry output value Growth of output value of the primary industry Primary industry growth rate Secondary industry output value Growth of output value of the secondary industry Secondary industry growth rate Tertiary industry output value Growth of output value of the tertiary industry Tertiary industry growth rate
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Table C1 Environmental Impact Subsystem Equations and Parameters. Variable
Unit
Type
Equation and parameters
Industrial exhaust emissions
Ten thousand tons Ten thousand tons %
Level variable
INTEG((Industrial exhaust growth)(13603.4))
Rate variable
Secondary industry output value *100 million RMB industrial exhaust production rate
Lookup function Level variable
WITHLOOKUP((Time)([(2007,-1)-(2022,1)],(2007,0.0120815),(2008,0.052323),(2009,0.117859), (2010,0.0608235),(2011,-0.00323594),(2012,0.022271),(2013,0.0182609),(2022,0.0400548))) INTEG((Solid waste growth)(3852.4))
Rate variable
Level variable
Secondary industry output value * billion RMB solid waste production rate WITHLOOKUP((Time)([(2007,-1)-(2022,1)],(2007,0.0612698),(2008,-0.00498319), (2009,0.0369836),(2010,0.017216),(2011,0.00446086),(2012,-0.00196774),(2013,-0.00850894), (2022,0.0149244))) INTEG((COD growth)(101.7))
Rate variable
Secondary industry output value * Billion RMB COD production rate
Lookup function
WITHLOOKUP((Time)([(2007,-1)-(2022,2)],(2007,-0.000333654),(2008,-0.00028321),(2009,0.000273034),(2010,0.00449615),(2011,-0.000312452),(2012,-0.000253309),(2013,0.000218319),(2022,0.0004032)))
Industrial exhaust growth Billion RMB industrial exhaust production rate Solid waste emissions Solid waste growth Billion RMB solid waste production rate COD emissions
Ten thousand tons Ten thousand tons %
Lookup function
Ten thousand tons Ten thousand tons %
COD growth Billion RMB COD production rate
Table D1 Resource Consumption Subsystem Equations and Parameters. Variable
Unit
Type
Equation and parameters
Biological substance Biological substance growth Biological substance growth rate
Ten thousand tons Ten thousand tons %
Level variable Rate variable Lookup function
Fossil fuels Fossil fuels growth Fossil fuels growth rate
Ten thousand tons Ten thousand tons %
Level variable Rate variable Lookup function
Building minerals Building minerals growth Building minerals growth rate
Ten thousand tons Ten thousand tons %
Level variable Rate variable Lookup function
Hidden flow total
Ten thousand tons
Regional hidden flow Regional hidden flow growth Regional hidden flow growth rate Import hidden flow Import hidden flow growth Import hidden flow growth rate Export hidden flow Export hidden flow growth Export hidden flow growth rate
Ten thousand tons Ten thousand tons %
Auxiliary variables Level variable Rate variable Lookup function
INTEG((Biological substance growth)(4448.32)) Biological substance * biological substance growth rate WITHLOOKUP((Time)([(2007,-0.2)-(2022,0.3)],(2007,0.0618705),(2008,-0.0265691), (2009,0.0558281),(2010,0.0964398),(2011,0.0338028),(2012,0.0557619),(2013,0.0539304), (2022,0.06))) INTEG((Fossil fuels growth)(3923.53)) Fossil fuels * fossil fuels growth rate WITHLOOKUP((Time)([(2007,-0.2)-(2022,0.3)],(2007,0.125158),(2008,-0.0051511), (2009,0.199697),(2010,-0.0801174),(2011,0.049953),(2012,0.0647727),(2013,0.0324943), (2022,0.1))) INTEG((Building minerals growth)(10046.1)) Building minerals * building minerals growth rate WITHLOOKUP((Time)([(2007,-0.5)-(2022,3)],(2007,0.0437971),(2008,2.39857),(2009,0.0898571), (2010,-0.299325),(2011,0.00722233),(2012,0.124061),(2013,0.0676104),(2022,0.455186))) Export hidden flow + regional hidden flow + import hidden flow
Ten thousand tons Ten thousand tons %
Level variable Rate variable Lookup function
Ten thousand tons Ten thousand tons %
Level variable Rate variable Lookup function
INTEG((Regional hidden flow growth)(148715)) Regional hidden flow * regional hidden flow growth rate WITHLOOKUP((Time)([(2007,-1)-(2022,1)],(2007,0.129311),(2008,0.411375),(2009,0.187997), (2010,-0.121941),(2011,0.0537355),(2012,0.13136),(2013,0.054019),(2022,0.1613))) INTEG((Import hidden flow growth)(22744.6)) Import hidden flow * import hidden flow growth rate WITHLOOKUP((Time)([(2007,-1)-(2022,1)],(2007,0.0377039),(2008,0.0308238),(2009,-0.124777), (2010,-0.11091),(2011,-0.0116003),(2012,0.13532),(2013,-0.0655004),(2022,-0.0155629))) INTEG((Export hidden flow growth)(4203.58)) Export hidden flow * export hidden flow growth rate WITHLOOKUP((Time)([(2007,-1)-(2022,1)],(2007,0.975685),(2008,0.270643),(2009,-0.176216), (2010,0.0547354),(2011,-0.295468),(2012,0.005011),(2013,-0.224004),(2022,0.0871981)))
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Table E1 Intensity Efficiency Subsystem Equations and Parameters. Variable
Unit
Type
Equation and parameters
Import Import growth Import growth rate
Ten thousand tons Ten thousand tons %
Level variable Rate variable Lookup function
Export Export growth Export growth rate
Ten thousand tons Ten thousand tons %
Level variable Rate variable Lookup function
DMI
Ten thousand tons
TMI
Ten thousand tons
DMO
Ten thousand tons
TMO
Ten thousand tons
DMI of 10,000 RMB of GDP TMI of 10,000 RMB of GDP DMO of 10,000 RMB of GDP TMO of 10,000 RMB of GDP DMI per capita
Tons RMB Tons RMB Tons RMB Tons RMB Tons
TMI per capita
Tons / person
DMO per capita
Tons / person
TMO per capita
Tons / person
Auxiliary variables Auxiliary variables Auxiliary variables Auxiliary variables Auxiliary variables Auxiliary variables Auxiliary variables Auxiliary variables Auxiliary variables Auxiliary variables Auxiliary variables Auxiliary variables
INTEG((Import growth)(5755.94)) Import * import growth rate WITHLOOKUP((Time)([(2007,-1)-(2022,1)],(2007,-0.051135),(2008,0.0767246),(2009,-0.046568), (2010,-0.0364415),(2011,0.0749748),(2012,0.0909905),(2013,0.193661),(2022,0.0431724))) INTEG((Export growth)(596.1)) Export * export growth rate WITHLOOKUP((Time)([(2007,-1)-(2022,1)],(2007,0.41384),(2008,0.164205),(2009,-0.125257), (2010,0.0329729),(2011,-0.198279),(2012,0.00538837),(2013,-0.155201),(2022,0.0196669))) Fossil fuels + building minerals + Biological substances + imports
/ ten thousand / ten thousand / ten thousand / ten thousand / person
DMI + regional hidden flow + import hidden flow Exports + solid waste emissions + industrial exhaust emissions Export hidden flow + regional hidden flow + DMO DMI / total GDP TMI / total GDP DMO / total GDP TMO / total GDP DMI /population TMI / population DMO / population TMO / population
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