Identifying a pathway towards green growth of Chinese industrial regions based on a system dynamics approach

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Identifying a pathway towards green growth of Chinese industrial regions based on a system dynamics approach Ling-ling Guo a,∗ , Ying Qu a , Chun-you Wu a , Xiao-ling Wang b a b

Faculty of Management and Economics, Dalian University of Technology, Dalian 116024, China Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China

a r t i c l e

i n f o

Article history: Received 30 March 2016 Received in revised form 30 September 2016 Accepted 30 September 2016 Available online xxx Keywords: Green growth System dynamics Scenario analysis Industrial regions Liaoning province

a b s t r a c t Green growth is considered a new growth mode to achieve sustainable development worldwide. There are existing studies that analyze the question of how to effectively configure resources, and balance economic development and environmental protection to achieve green growth. However, these studies are mainly focused on discussing effects of three elements on green growth, namely, economy, resources, and environment, without considering dependent interactions among the three elements themselves, and other essential elements, such as population, policy and so on. This paper develops a system dynamics model integrated with five subsystems including economy, population, resources, environment, and policy to investigate pathways towards green growth in a typical Chinese traditional industrial region, Liaoning province. Based on the current policy tools, five path scenarios are considered for policy makers, namely, business as usual scenario, technical innovation scenario, environmental regulations and management scenario, growth demand management scenario, and comprehensive optimization path scenario. Considering respective development objectives of these scenarios, this paper summarizes and analyzes characteristics of the five scenarios by examining long-term prospects of green GDP, energy consumption, and CO2 emissions. According to the ranking results for these five scenarios, with respect to energy consumption per GDP, energy consumption mix, CO2 emissions per GDP, cost of resources and environment, and green GDP per capita, the comprehensive optimization path scenario is identified as the most appropriate and effective to achieve green growth in Liaoning province. Finally, policy implications are offered based on the simulation results. © 2016 Elsevier B.V. All rights reserved.

1. Introduction In order to address resource shortage and climate changes, an increasing number of countries start seeking out new growth modes to realize sustainable development. The governments of the countries, especially developing countries, try to find effective methods to reduce energy consumption and CO2 emissions, and improve environmental conditions. Many countries started to implement green growth (GG) strategies on country levels, and the “Global Green New Deal” has been introduced worldwide. GG, as a new mode of economic growth, was first proposed by the United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP) in 2005 (ESCAP, 2005). It was defined as a strategy to promote “win-win” approaches and policies for

∗ Corresponding author at: Faculty of Management and Economics, Dalian University of Technology, No. 2 Linggong Road, Dalian, 116024, China. E-mail address: guolingling [email protected] (L.-l. Guo).

reconciling conflicts between two important Millennium Development Goals on poverty reduction and environmental sustainability (ESCAP, 2006). Subsequently, the Organization for Economic Cooperation and Development (OECD) described GG as “fostering economic growth and development while ensuring that natural assets continue to provide resources and environmental services on which our well-being relies” (OECD, 2011), where “to do this, it must catalyze investment and innovation which will underpin sustained growth and give rise to new economic opportunities” (OECD, 2011). A consensus view of these two different definitions is that GG fully meets transformation objectives and requirements of conventional economic growth patterns. Since the introduction of GG, the concept was widely accepted by many countries, especially developed countries. For example, South Korea (Mathews, 2012), the UK (HM Government, 2009), Germany (Jänicke, 2012) and Canada (Webb and Esakin, 2011) adopted certain GG practices in order to achieve economic sustainability and low-carbon development. Since the opening and reforms, China’s average annual growth rate of GDP reached 9.81%, far above the world average level of

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2.87%. However, rapid development also caused severe natural resource depletion and environmental pollutions. For instance, the total energy consumption in China increased by 207% from 1.36 billion tce in 1997 to 4.17 billion tce in 2013 (China’s National Bureau of Statistics, 2014a); waste water discharge increased by 1572% from 41.58 billion tons in 1997 to 695.44 billion tons in 2013; industrial waste gas emissions increased by 525% from 10.7 trillion cubic meters in 1998 to 66.9 trillion cubic meters in 2013; and industrial solid waste generation increased by 212% from 1.06 billion tons in 1997 to 3.31 million tons in 2013 (China’s National Bureau of Statistics, 2014b). In fact, according to experience in developed countries, the coordination of economy and environment can be greatly improved by implementing GG strategies (HM Government, 2009; Jänicke, 2012). Inspired by these significant achievements, Chinese government started to launch its own GG strategy in 2012 (The Eighteenth CPC National Congress, 2012). In the bulletin of the fifth plenary sessions of the 18th Central Committee of the Communist Party (Chinese Communist Party, 2015), the government specifically proposed to implement the green development idea during the 13th Five Year Plan, which demonstrates the government’s determination in pursuing GG. Therefore, the identification of effective pathways towards GG becomes significantly important for Chinese regions, especially traditional industrial areas suffering most serious natural resources exploitation and environmental pollutions. For these areas, it is crucial to implement the country’s GG strategy. Currently, there are several studies on the achievement of GG (OECD, 2010; Ploeg and Withagen, 2013). Based on successful GG experience in OECD member countries, OECD (2010) found that good policies had positive effects on improving regional green growth. Ploeg and Withagen (2013) indicated that the combination of R&D subsidies and carbon taxes was the best pathway to achieve green growth. Grover (2013) investigated India’s GG strategy, and discovered that there was a significant relationship between green innovations and GG. Lee and Kim (2016) indicated that the momentum from local governments and active citizen participation played key roles in implementing Korea’s green energy strategy. However, most studies focused on some specific pathways towards GG, such as a policy, R&D subsidies, innovation and so on. Only several attempts were made to integrate some pathway factors in a methodology framework by adopting theories and methods, such as the game theory (Carfì and Schilirò, 2012), statistical analysis (Kim et al., 2014; Zaman et al., 2016) and CGE models (Dai et al., 2016). Carfì and Schilirò (2012) devised an appropriate game theory model to address relationships among climate change policies, low-carbon technologies, and green economy, where a fair Kalai-Pareto solution of the competitive model for a win–win scenario was developed. Zaman et al. (2016) adopted econometric modeling techniques to examine effects of energy consumption, environment, health and wealth on BRICS countries’ green growth in 1975–2013. Dai et al. (2016) used a dynamic computable general equilibrium (CGE) model to assess economic and environmental impact of large-scale development of renewable energy on green growth in Chinese power sector. The results indicate that large-scale renewable energy development has significant effects on green growth that benefits the development of upstream industries, reshapes the energy structure, and brings substantial environmental co-benefits. However, these existing studies are limited, since economy, resources and environment are major factors impacting the GG system. GG involves many complicate, dynamic and systematic activities, and is definitely influenced by various factors including population and policy (Musango et al., 2014). Therefore, it is extremely important to build an integrated system including economy, population, resources, pollution and policy to explore and identify pathways to achieve GG, which would make research results more close to the reality.

With this goal, a system dynamics (SD) method was applied to investigate the GG system. Our study is one of the first trails that explore dependent interactions among major influencing factors, discuss how the factors of economy, population, resources, environment and policies affect GG’s implementation, and further analyze dynamic change characteristics of major influencing factors in order to identify appropriate and feasible pathways to achieve Chinese GG. Also, our research contributes to examining prospects of long-term green economic growth, energy consumption and structure, and CO2 emissions in China. Using visual observations, the disparity between the current development mode and GG strategy can be confirmed. Additionally, this study can provide decision-makers, local governments and managers with some implications from the following aspects. First, the effects of environmental policies on GG are discussed in this paper. Therefore, for those governors who intend to protect their countries or regions facing severe resource shortage and environmental pollution, a strategy for enhancing environmental laws and regulations is given in the process of GG implementation. Moreover, although the SD framework is used for the case of Liaoning province, China, the structure of the model is similar to GG systems in many regions. Therefore, by changing values of some parameters, the SD model becomes also appropriate for other regions of China. The reminder of the paper is organized as follows. Literature review is presented in Section 2. The method is described in Section 3, including data and case background, problem analysis, and the SD model. Section 4 summarizes simulation results based on the model. Section 5 provides policy implications. Finally, Section 6 discusses main conclusions.

2. Literature review Compared to other methodologies, a SD model is chosen in this paper to identify pathways towards GG for two reasons. First, the method has excellent advantages in dealing with nonlinear, high order complex system problems, especially in analyzing socioeconomic-environmental systems (Sahin et al., 2015). Second, with the help of computer simulation techniques, the SD method can visually analyze relationships among various factors, simulate data, as well as describe information related to feedback structures, functions, and behavior of the system (Liu et al., 2015a,b). The model is much easier to apply to simulate various path scenarios to reveal dynamic evolution mechanisms of the GG system. The SD method was first proposed by J.W. Forrester and his colleagues in 1956, and was specifically designed to analyze complex dynamic feedback systems (Liu et al., 2015a,b). SD models are widely used in various fields, including economic-environmental systems (Niedertscheider et al., 2014), energy management and pollution control (Liu et al., 2015a,b), industrial policy and planning (Ansari and Seifi, 2012) and others. For instance, for economicenvironmental systems, F.J. Li et al. (2012) and F. Li et al. (2012) applied a SD model to analyze environmental and economic effects of the eco-agriculture system of Kongtong District, Pingliang City, Gansu Province, China, from 2009 to 2050. Zhang et al. (2014) built a SD simulation model for a regional ecological water carrying capacity to investigate the coordination development of social economy and water eco-environment in Siping area of Jilin Province, China. For energy management and pollution control, the SD method was widely applied to investigate the performance of energy consumption and CO2 emissions on the national, regional and industrial levels (Ansari and Seifi, 2013; Wang et al., 2016). Ansari and Seifi (2013) employed a SD model to study energy consumption and CO2 emission under different production and export scenarios in Iranian cement industry. Their simulation results indicate

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that complete removal of energy subsidies and implementation of corrective policies can potentially lead to reduction in natural gas and electricity consumption by 29% and 21%, respectively, and reduction in CO2 emission by 22%. Feng et al. (2013) developed a SD model using the STELLA platform to simulate trends of energy consumption and CO2 emission in Beijing, China, over 2005–2030. The results show that a change in the energy structure may play an essential role in reducing carbon emission of Beijing. Robalino-López et al. (2014) presented a SD model of CO2 emissions in Ecuador to study how changes in the energy matrix and GDP affect CO2 emissions in 1980–2020. In addition, the SD method was also used in energy and environmental policy and regulation decision-making (F.J. Li et al., 2012; F. Li et al., 2012). Dace et al. (2015) built a SD model based on the IPCC guidelines and included main elements of an agricultural system to assess effects of various policy decisions and measures on agricultural GHG emissions in Latvia. For industrial policy and planning, the SD method is used to discuss effects of environmental policies, such as cleaner production policies and waste disposal charging policies, on high emission industries (Dong et al., 2012; Yuan and Wang, 2014). For instance, Yuan and Wang (2014) developed a SD model to determine appropriate waste disposal charging fees in the construction sector of Shenzhen, China. Additionally, a SD model was also applied to research usage of incentives policies in low emission and emerging industries, such as the photovoltaic power industry, and recycling and remanufacturing industry (Wang et al., 2014; Dace et al., 2014; Guo and Guo, 2015). For example, Guo and Guo (2015) presented a SD model to explore the impact of subsidy policies on the development of the recycling and remanufacturing industry related to Chinese auto parts. Their research findings show that mixed-subsidy policies have better positive effect on remanufacturing promotion than single subsidy policies, but they also involve higher costs. Previous studies showed how economic, environmental and political elements can be integrated in a SD model, and provided a certain foundation for further GG research. However, most existing research only focuses on optimization of economy and environment. With GG becoming a hot topic in academic circles, some researchers have started to employ the SD method to explore national and regional GG (Musango et al., 2014; Kuai et al., 2015). For example, Musango et al. (2014) developed an integrated system dynamics model to assess the green transition in South Africa, where four scenarios were simulated, and the most effective policy was identified. Kuai et al. (2015) used a SD model to evaluate different green transformation alternatives concerning three prominent factors, namely, the industrial scale, structure, and efficiency, in Linfen city, China. In addition, the SD method was also applied to research green transportation (Trappey et al., 2012), green power (Qudrat-Ullah, 2014), green building (Onat et al., 2014) and green supply chains (Tian et al., 2014). For instance, Trappey et al. (2012) designed a SD model for green transportation policies to evaluate the green transportation system and related policy effects in a smaller, controlled environment for Taiwan Island. Qudrat-Ullah (2014) created a green power SD model to evaluate long-term dynamics of Ontario’s electricity sector in the socio-economic and CO2 emissions dimensions. A similar research was done in Blumberga et al. (2014). Onat et al. (2014) utilized the SD method to analyze middle and long-term effect of green building relevant policies on the GHG emissions stock. The results show that only enhancing the construction rate of net zero or high performance green buildings is not conducive to stabilizing/reducing the GHG emissions trend unless retrofitting the existing residential building stock is severely identified as a strict policy along green building policies. A similar study was conducted by Teng et al. (2016). Tian et al. (2014) developed a SD model to study impacts of sub-

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sidy policies on the diffusion of green supply chain management in Chinese automotive manufacturing industry. The research results indicate that subsidies for manufacturers are better than those for consumers to promote green supply chain management diffusion, where environmental awareness is another influential key factor. However, in spite of these studies, the literature on GG using the SD approach is scarce, especially regarding pathways to achieve GG. In addition, previous research rarely simulates dynamic interactions of economic growth, population development, resource consumption, environmental pollution, and policy responses in an integrated system model. To fill in these gaps and contribute to the existing literature, this paper aims to develop a SD model for the GG system with five subsystems, including economy, population, resource, environment, and policy, for a traditional industrial region, Liaoning province, China. In order to identify an appropriate path towards GG, the paper examines the prospects of long-term green GDP, energy consumption, and CO2 emissions under different path scenarios, and compares different simulation results for these scenarios. 3. Method 3.1. Data and case background Liaoning, a typical Chinese industrial province, is taken as the subject of the case study using a SD model. As shown in Fig. 1, Liaoning province is located in Northeast China, and has the most comprehensive categories of industry in the country. Among them, the equipment manufacturing and raw material industries in Liaoning are more developed than in other provinces, playing an important role in the country economics. In 2014, the GDP growth rate of Liaoning was 5.8%, and 44.2% of the economic added value was created by the secondary industry (Liaoning Municipal Bureau of Statistics, 2015). Although the industries have enormous contribution to the economic growth, they cause problems related to resource consumption and pollutants discharge. For example, in 2014, the total energy consumption of the province was 205.86 million tce, and the industries consumed 60.7% of this amount. The total SO2 , and smoke and dust emissions were 0.99 and 1.12 million tons, respectively, ranked the sixth and fourth in the country, respectively (Liaoning Municipal Bureau of Statistics, 2015). Because of the serious air pollution problems, two cities in the Liaoning province, Liaoyang and Jinzhou, were ranked the twelfth and sixteenth out of 258 major cities in terms of air pollution (China’s Ministry of Environmental Protection, 2014). In addition, four cities and three municipal districts in Liaoning province were identified as resource-exhausted cities by the State Council of China (The State Council, 2012). To fully reinvigorate economic growth and improve environmental quality, the local government tries to explore new sustainable paths during the 13th Five Year Plan. In this paper, the parameters and variables of the model are mostly taken from investigated and statistical data. The statistical data on economic, population, resources, and R&D indicators was mainly obtained from the Liaoning Statistical Yearbook (2002–2015), and the data on environmental investment indicators was originally taken from the China Statistical Yearbook on Environment (2002–2014). Some empirical formulas were also adopted for data due to lack of sufficient data resources. 3.2. System structure analysis The SD method is a computer simulation modeling technique used to frame, understand, and discuss complex issues. In general, the SD method includes the following steps: system structure anal-

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Fig. 1. Location of Liaoning province in China.

ysis, model development, model validation, and scenario definition (Kuai et al., 2015). The system structure analysis is a premise of the system dynamics modeling. It incorporates qualitative description of relationships among elements included in the system, which can help the modeler become familiar with the GG system under study. The system structure analysis contains the following steps: determining system boundaries, defining system variables and parameters, and constructing causal feedback. In this case, the purpose of simulation is to find an appropriate and feasible pathway to achieve GG based on the five aspects including economy, population, resources, environment, and policy. Therefore, a model is developed within the boundaries of social-economic growth, corresponding resource and environmental performance, and policy response. Excessive consumption of resources and severe environmental pollution are external pressures of GG. According to the concept of GG, its achievement is mainly affected by two drivers, namely, technical innovations and capital investments, which are external policy response elements (OECD, 2011). Based on policy measures, energy savings and emission reductions can be achieved, which will further promote GG. The first set of variables focuses on economic and population growth. The second set of variables focusing on environmental and resource objectives, is defined as intensities of energy consumption and CO2 emissions. The third set of policy variables considers R&D expenditure, environmental protection investments, and pollution control investments. In addition, other variables and parameters are used as auxiliary variables. In the GG system, resource inputs and pollution outputs are driven by rapid economic growth. The faster the growth rate, the higher resource inputs and pollution outputs. Technical innovations and investments may influence the efficiency of economic growth. When technology expenditure is directed toward pollution

treatment, and environmental investments are increased, environmental costs become lower, and the net GDP growth rate becomes larger; and vice versa. This is what constitutes the feedback from external pressures and external policies. Adjustments can be made to the system to meet economic, environmental and resource objectives. All variables are described in Fig. 2. 3.3. Model development A flowchart of the stock-and-flow diagram is developed within the SD model after analyzing the system structure (see Fig. 3), including three types of variables, namely, the level, rate, and auxiliary variables. Level variables describe the cumulative effects of the system, and depend on the accumulation of materials, energy, and information over time. Rate variables reflect changes in level variables over time, and represent the speed of such changes in the system. Auxiliary variables are intermediate variables used

Fig. 2. System analysis framework of Liaoning’s GG system.

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energy consumption per capita energy consumption per unit of GDP GDP per capita

energy consumption



coal consumption

oil consumption

share of renewable energy consumption

gas consumption

GDP GDP growth rate



R&D expenditure

R&D expenditure as a share of GDP carbon tax

coal price

gas price

renewable energy consumption

carbon tax revenues

gas consumption cost

coal consumption cost

carbon dioxide emissions per capita



population population growth rate

environmental pollution environmental pollution treatment investment as a green GDP per treatment investemnt share of GDP captica

oil consumption cost

carbon dioxide emissions per unit of GDP

carbon dioxide emissions

environmental cost oil price

5

cosumption cost

cost of resources and environment



green GDP

Fig. 3. Stock and flow diagram of Liaoning’s GG system.

throughout the decision-making process. According to the system relationships determined by the system analysis of the Liaoning case, a SD model is developed with 5 subsystems, 2 level variables, 2 rate variables, and 29 auxiliary variables. In this paper, the simulation time is set to be from 2001 to 2025. Accordingly, the simulation results obtained for the period of 2001–2014 are used for testing, and those for 2015–2025 are applied to evaluate path scenarios.

3.3.1. Economy subsystem Due to the traditional extensive mode of economic growth, the problems of resource depletion and environmental pollution become more and more serious (Li et al., 2016). Accordingly, a flow diagram of the economy subsystem is built, which is shown in Fig. 2. The initial GDP growth rate during 2001–2014 is obtained from the Liaoning Statistical Yearbook. Based on the Liaoning 13th Five Year Plan, the GDP growth rate from 2016 to 2020 is set as 6.6%. The growth rate in other years is obtained using the double exponential smoothing model. In the economy subsystem, the concept of green GDP is introduced, which is used to accurately understand interactions between the economy and environment (UN et al., 2012). According to the measurements of the green GDP obtained from the National Environmental Protection Administration and National Bureau of Statistics, the green GDP equals the GDP minus costs of resources and environmental degradation (State Environmental Protection Administration and China’s National Bureau of Statistics, 2006). The equation (except for the depreciation of fixed assets) is shown below. Ggdp = G − C = G − (RC + EC)

(1)

where Ggdp is the green GDP; G represents the GDP; C is the cost of resources and environment; RC is the resource consumption cost, and EC is the environmental loss cost. According to Zhao and Sun (2016), there is a positive relationship between environmental loss cost and environmental pollution treatment investments. Higher pollution levels imply higher environmental losses, and greater environmental treatment funds. Based on availability, data on environmental loss cost was obtained from environmental pollution treatment investments, and data on resource consumption cost was calculated using energy consump-

tion and energy prices. In addition, the green GDP per capita is the ratio of the green GDP and the total population. 3.3.2. Population subsystem The population scale and its growth rate exert important impact on the economy subsystem, and resource and environment subsystem (Musango et al., 2014). Rapid expansion of population implies that more people become available to create wealth, and to share the wealth at the same time. Under the dual pressure of economic and population growth, the demand for natural resources increases and destruction of ecological environment becomes worse. The flow diagram of the population subsystem is shown in Fig. 3. For this subsystem, we obtained the initial population growth rate during 2001–2014 from the Liaoning Statistical Yearbook. Additionally, we assumed that the population growth rate was 2‰ during 2015–2020, and 2.5‰ during 2021–2025 due to the two-child policy in the country (Health and Family Planning Commission, 2015). 3.3.3. Resource and environment subsystem Based on the study of Kim et al. (2014), the variables of energy consumption and CO2 emissions are often used as major research objects of the resource and environmental subsystem. Due to availability of data, we just considered traditional energy resources including coal, oil, gas, and renewable energy, such as hydropower, nuclear and wind energy. The energy consumption per unit of GDP is the total energy consumption divided by the GDP, and the energy consumption per capita is the total energy consumption divided by the population size. As indicated in Fig. 3, CO2 emissions are calculated based on coal, oil, gas, and renewable energy consumption. The specific equation recommended by the IPCC is as follows. 4 

CEj = (44/12)

Eij × Fi

(2)

i=1

where CEj represents CO2 emissions in year j; j represents the year, i is the energy type, Eij is the energy consumption of energy type i in year j, and Fi is the carbon-emission factor of energy type i. The parameter of 44/12 is the conversion coefficient between carbon and carbon dioxide.

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Based on the research results of the Energy Research Institute (The Research Group of National Development and Reform Commission Energy Research Institute, 2009), and USDOE, EIA and OIAF (USDOE et al., 2007), Fi is set at 0.7329, 0.565, 0.445, and 0.5664 for coal, oil, natural gas and renewable energy, respectively. In addition, CO2 emissions per unit of GDP are the total CO2 emissions divided by the GDP, and CO2 emissions per capita are the total CO2 emissions divided by the population size. 3.3.4. Policy subsystem According to the research made by the OECD (2010), good policy measures, such as taxes, investments, subsidies and innovations, are identified as key elements to promote GG strategies. Additionally, Yi (2013) analyzed clean energy policies in major U.S. cities, and the results showed that national clean energy tools and local climate policies had positive impact on green jobs. Accordingly, the flow diagram of the policy subsystem is built, see Fig. 2. In the subsystem, the variables of R&D expenditure and carbon tax are included. The variable of R&D expenditure is calculated as the product of their proportion of GDP and GDP, and it establishes the relationship with the resource and environment subsystem using the variable of renewable energy consumption. Until now, the carbon tax is not imposed in China, so the initial data on the carbon tax is set as 0 from 2001 to 2025 in this paper. 3.4. Model validation To ensure whether the SD model is consistent with the reality, the dimensional consistency, historical, and sensitivity tests were conducted using the VENSIM DSS, a professional computer simulation software set. Though some parameters of the SD model have no realistic meaning, the model can still maintain dimensional consistency. Based on the historical test, the error rates of the GDP, population, energy consumption, and R&D expenditure based on the difference between historical and simulation values were calculated. The results are demonstrated in Appendix A. The mean error rates for these four variables are 0.002%, 0.0000%, 0.47%, and −3.11%, respectively, and their absolute error rates were all within 10% (except for the R&D expenditures in 2001 and 2002, where they were slightly higher). This indicates that the SD model exhibited good historical fitness. The sensitivity test was carried out to examine subtle effects of changes in some parameters on the system’s behavior. The values of the indices used in the sensitivity test were adjusted randomly in a certain proportion. The ranges of uncertainties are explained when the index values are adjusted. In this paper, the R&D expenditure as a share of GDP is chosen to conduct the sensitivity test by adjusting its value. The results are presented in Appendix A. 3.5. Scenarios Based on the results of the model validation tests and current policy tools, five path scenarios were considered, namely, business as usual (BAU) scenario, technical innovation (TI) scenario, environmental regulations and management (ERM) scenario, growth demand management (GDM) scenario, and comprehensive optimization path (COP) scenario. Among these scenarios, the TI represents the clean and renewable energy technologies strategy. The ERM is the carbon trading and ecological compensation strategy, and the GDM is a typical economic development management strategy. 3.5.1. BAU scenario The BAU scenario is a general continuation of the current development conditions without any additional policy benefits. This

means that related settings for the five subsystems were the same as above.

3.5.2. TI scenario Liaoning province is one of China’s largest energy consumers. Its main source of energy is coal, followed by oil. Coal contributed to 70.8% of the total energy consumption from 2001 to 2014, and oil accounted for 24.4% during the same period (Liaoning Municipal Bureau of Statistics, 2015). Accordingly, Liaoning province faced the urgent task of creating sustainable energy structures. In recent years, Liaoning made great efforts to use clean and renewable energy. In 2001–2014, the proportion of renewable energy in Liaoning province increased from 0.3% to 1.59%, and the R&D expenditure as a share of the GDP increased from 1.07% to 1.5% (Liaoning Municipal Bureau of Statistics, 2015). According to the studies of the Development Research Center of the State Council, the proportion of renewable energy in Liaoning province will increase to 3.3% by 2020 (Pan et al., 2015). In order to achieve this goal, technological spending needs a boost as well. In the TI scenario, the R&D expenditure as a share of GDP was assumed to be 2% in 2016–2025.

3.5.3. ERM scenario Liaoning province took a series of environmental policy decisions to cope with pollution problems. First, on May 21, 2012, Liaoning Environmental Protection Bureau (LEPB) (LEPB, 2012) officially released “the interim provisions on urban environmental air quality assessment in Liaoning province”. Under this new regulation, Liaoning province conducted a restraining policy on the daily average concentration of SO2 , NO2 and PM10. If the daily average concentration of SO2 , NO2 and PM10 in a city exceeded the secondary standard 0.25, 0.25, and 0.5 times, respectively (in accordance with “the ambient air quality standard” (GB3095-1996)), the LEPB would give the city “Red Notice” and issue a fine accordingly (LEPB, 2012). Second, “the interim provisions on discharge permit management in Liaoning province”, announced by the LEPB on June 9, 2015, required polluters not only to provide the type, concentration, and capacity of their discharge, but also to present how, when, and where they treated the discharges (LEPB, 2015). In addition to promulgate environmental regulations, Liaoning also invested large amounts of money in environmental pollution treatment. The environmental pollution treatment investments increased from 4.85 billion Yuan to 34.76 billion Yuan during 2001–2013, by more than six times (China’s National Bureau of Statistics, 2014b). In future development, the question of how to effectively control greenhouse gas emissions and minimize consumption will become important based on international successful practices, such as Finland, Denmark, the Netherlands, Norway, and Sweden (Liu and Li, 2011). In December 2012, the National Development and Reform Commission (NDRC) formally announced Beijing, Tianjin, Shanghai, Chongqing, Guangdong, Hubei province, and Shenzhen as carbon emission trading pilots in order to achieve China’s carbon reduction target by 2020. Based on this, it is reasonable to believe that a carbon tax policy will be accepted in some Chinese provinces and cities in the near future. In the ERM scenario, we assume that the carbon tax is to be imposed in Liaoning province during the 13th and 14th Five Year periods. According to an advice of the Ministry of National Treasury, the carbon tax rate should be set as ¥40 per ton in 2020. However, the Ministry of Environmental Protection recommended that the carbon tax rate should be ¥50 per ton by 2020 (National Development and Reform Commission and Ministry of Finance, 2010). Considering these two advices, the carbon tax rate was set to be ¥45 per ton for years 2016–2020, and ¥50 per ton for years 2020–2025 in the ERM scenario.

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Table 1 Energy consumption and energy consumption per unit of GDP in different scenarios during 2016–2025. Year

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Energy consumption (million tce)

Energy consumption per unit of GDP (tce/10000 Yuan)

BAU

TI

ERM

GDM

COP

BAU

TI

ERM

GDM

COP

232.42 238.40 244.58 250.97 257.54 264.26 271.20 278.21 285.19 292.00

235.33 241.64 248.19 254.99 262.02 269.25 276.76 284.41 292.13 299.76

232.42 238.40 244.58 250.97 257.54 264.26 271.20 278.21 285.19 292.00

232.42 238.40 244.58 250.97 257.54 264.26 272.00 279.77 287.42 294.75

235.33 241.64 248.19 254.99 262.02 269.25 277.63 286.13 294.61 302.87

0.73 0.70 0.68 0.65 0.63 0.60 0.58 0.56 0.54 0.51

0.74 0.71 0.69 0.66 0.64 0.62 0.59 0.57 0.55 0.53

0.73 0.70 0.68 0.65 0.63 0.60 0.58 0.56 0.54 0.51

0.73 0.70 0.68 0.65 0.63 0.60 0.58 0.55 0.53 0.50

0.74 0.71 0.69 0.66 0.64 0.62 0.59 0.57 0.54 0.52

3.5.4. GDM scenario Since the strategy of rejuvenating the northeastern old industrial base was initiated by Chinese government in 2003, Liaoning’s economy gained rapid growth. In 2004–2012, the GDP growth rates were all higher than 11.5% in 2003. However, the growth trend started to decline in 2012, and the GDP growth rate dropped to 5.8% by 2014 (Liaoning Municipal Bureau of Statistics, 2015). Recently, the CPC Central Committee and the State Council deliberated and passed “several options on comprehensively rejuvenating the old northeastern industrial base” (CPC Central Committee State Council, 2015). As a result, Liaoning province will gain golden development opportunities, and the GDP growth rate will likely rebound in the future. Accordingly, in the GDM scenario, we set the GDP growth rate as 7.5% for 2021–2025.

3.5.5. COP scenario The COP scenario is a combination of the TI, ERM, and GDM scenarios.

4. Results According to the connotation of GG, sustainable economic growth, resources savings, and pollution reduction are the main goals of GG (OECD, 2011). Therefore, evaluation of different scenarios was conducted based on the energy consumption, CO2 emissions, and green GDP.

4.1. Energy consumption Table 1 demonstrates the simulation results for the energy consumption and energy consumption per GDP in different scenarios. The results indicate that the total energy consumption presents a continuing increasing trend from 2016 to 2025. The COP scenario shows the greatest improvement, for which the value increases from 235.33 to 302.87 million tce, and the second best is the TI scenario. The main reason for this is that renewable energy consumption increases sharply in these two scenarios, especially in the COP scenario. The most fundamental cause is that the share of R&D expenditure on clean and renewable energy increased from 1.5% to 2%. This indicates that increasing R&D expenditure by itself can neither effectively control total energy consumption, nor reduce energy consumption per unit of GDP in Liaoning. The energy consumption per unit of GDP under different scenarios shows a decreasing trend. The largest decrease is in the GDM scenario, down to 0.50 tce/10000 Yuan in 2025 from 0.73 tce/10000 Yuan in 2016, and the smallest decrease is in the TI scenario, down to 0.53 tce/10000 Yuan in 2025 from 0.74 tce/10000 Yuan in 2016. In the GDM scenario, the total energy consumption increased by 2.75 million tce with 7.5% GDP growth rate compared to 292.00 million tce in the BAU scenario in 2025. However, the energy consumption per unit of GDP in the GDM scenario declined to 0.50 tce/10000 Yuan in 2025. This explains why rapid economic growth can reduce energy consumption per GDP when the absolute values of energy consumption vary greatly in these five scenarios. Fig. 4 shows the changes of energy consumption mix under different scenarios from 2016 to 2025. As shown in Fig. 4, the shares

Fig. 4. Changes in energy consumption mix under different scenarios.

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Fig. 5. Total CO2 emissions, CO2 emissions per capita, and CO2 emissions per GDP in different scenarios.

of oil, gas and renewable energy consumption in different scenarios reveal an increasing trend during 2016–2025. The share of coal consumption shows a downward trend, though its absolute value of coal consumption is increasing. The results indicate that the energy mix in all scenarios is optimized as the share of coal consumption continuously decreases and that of gas and renewable energy consumption keeps increasing. Among the five scenarios, the share of renewable energy consumption in the COP scenario increased sharply from 3.21% in 2016 to 6.97% in 2025, and the share of coal consumption decreased largely with the values ranging from 61.87% in 2016 to 50.75% in 2025 due to the great increase in the R&D expenditure to some extent. The results indicate that the energy mix in the COP scenario is more reasonable than the others.

Fig. 6. Changes in cost of resources and environment under different scenarios during 2016–2025.

4.2. CO2 emissions

4.3. Green GDP

Fig. 5 shows changes in total CO2 emissions, CO2 emissions per capita, and CO2 emissions per GDP in different scenarios during 2016–2025. As shown in Fig. 5, in all scenarios, both total CO2 emissions and CO2 emissions per capita present increasing trends from 2016 to 2025, and changes in total CO2 emissions are similar to energy consumption. In addition, the COP scenario reveals largest increments, followed by the TI scenario, with values ranging from 571.89 million tons to 711.78 million tons, and from 13.43 ton per person to 16.39 ton per person, respectively. At the same time, the increments in the BAU and the ERM scenarios are minimal. The variable of CO2 emissions per GDP shows a declining tendency during 2016–2025 under different scenarios, where changes are similar to those in energy consumption per GDP. The fall in the GDM scenario is maximal, with decrease by 0.59 ton/10000 Yuan from 2016 to 2025, and in the TI scenario is minimal, with decrease by 0.56 ton/10000 Yuan. In the GDM scenario, the total CO2 emissions increased by 5.85 million tons with 7.5% GDP growth rate compared to 689.05 million tons in the BAU scenario in 2025, and the CO2 emissions per GDP in 2025 were cut down by 33.2% compared to those in 2016. In the BAU scenario, the CO2 emissions per GDP dropped to 1.21 ton/10000 Yuan, just 31.9% of those in 2016. This indicates that the effect of GDP growth rate on CO2 emissions reduction is not significant. Similarly, in the TI scenario, the high R&D spending rate cannot help to decrease emissions of CO2 , due to limitations in technology and the mode of clean energy production.

The changes in cost of resources and environment under different scenarios are forecasted as shown in Fig. 6. The results reveal that, during 2016–2025, all the scenarios present increasing trends. The increment in the ERM scenario is minimal, from 354.55 billion Yuan in 2016 to 523.69 billion Yuan in 2025. Though the increment in the COP scenario is maximal, the cost of resources and environment during 2016–2025 is only higher than those in the GDM scenario, and maintain a lower speed of growth. This indicates that though rapid GDP growth rates lead to increase in resource and environmental costs due to the traditional production mode, the introduction of the carbon tax makes a difference in terms of reducing the effect of economic growth on resources and environment. Fig. 7 depicts changes in the green GDP per capita in different scenarios. Accordingly, these five scenarios show increasing trends from 2016 to 2025. In 2025, the green GDP per capita in the COP scenario is the largest, followed by the GDM scenario. Additionally, the increase in the green GDP per capita in the TI scenario is minimal, from 6.57 10000 Yuan per person in 2016 to 11.78 10000 Yuan per person in 2025. Moreover, differences in the green GDP per capita are small among the five scenarios from 2016 to 2025.

5. Discussion and implications To determine an effective path scenario for achieving Liaoning’s GG, these five scenarios are compared and ranked based on the simulation results for energy consumption per GDP, energy

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Table 2 Ranking of these five scenarios. Rank

Energy consumption per GDP

Energy consumption mix

CO2 emissions per GDP

Cost of resources and environment

Green GDP per capita

1 2 3 4 5

GDM BAU and ERM (tied) BAU and ERM(tied) COP TI

COP TI GDM BAU and ERM (tied) BAU and ERM (tied)

GDM BAU and ERM (tied) BAU and ERM (tied) COP TI

ERM COP BAU GDM TI

COP GDM ERM BAU TI

Notes: The number in column 1 is ranked from best to worst (up to the bottom) for each performance aspect.

Fig. 7. Changes in the green GDP per capita in different scenarios from 2016 to 2025.

consumption mix, CO2 emissions per GDP, cost of resources and environment, and green GDP per capita (see Table 2). As shown in Table 2, in terms of energy saving and emissions reduction, the outcomes of the GDM scenario are the best, followed by the BAU and ERM scenarios. The COP scenario is located in a relatively moderate range, and the performance of the TI scenario is the worst, especially in terms of the green GDP per capita and cost of resources and environment. Regarding energy mix optimization and green GDP per capita, the COP scenario performs the best. However, this scenario is ranked 2nd in terms of the cost of resources and environment, only after the ERM scenario. Consequently, the COP scenario is determined as an appropriate alternative to achieve GG in Liaoning province, yet not the “best” one according to the ranking results. The main reasons for the selection are as follows. First, the disparity in energy consumption per GDP between the GDM and COP scenarios is only 0.02 in 2025, which is quite small. Moreover, the difference in CO2 emissions per GDP is also small between the COP scenario and the scenario ranked first. In addition, the CO2 emissions per GDP in the five scenarios in 2020 all dropped by about 60%, and successfully met the target of 40–45% reduction in 2005. Therefore, the COP scenario shows some advantages in terms of energy saving and emissions reduction. Second, Liaoning is a resource-based province, and optimization of its energy mix is crucial. In recent years, the government actively developed new and renewable energy and adjusted the energy structure. For example, Liaoning Hong Yanhe Nuclear Power Plant, the first nuclear power plant in the northeast China, was built in 2012 with generation capacity of more than 9600 KWH per day. Compared to others, the COP scenario of adjusting energy mix is the most effective one. Third, GG always focused on realizing sustainable economic development at the lowest cost of natural resources and environment (OECD, 2011). The COP scenario provides a valuable alternative. The green GDP per capita in this scenario is ranked first, but the cost of resources and environment is ranked second. This indicates that blind pursuit of the growth rate is unwise, whereas carbon taxes, green investments, and innovation policies should be in place to compensate losses in resources and environment.

Based on the results mentioned above, a series of policy measures for the TI, ERM, GDM and COP scenarios are recommended to achieve GG in the future. Specifically, the TI scenario is an effective path to optimize energy mix. In this scenario, the share of renewable energy consumption increased with the increase in research and development expenditure due to development of renewable energy technologies. Therefore, compensative or incentive policies, such as subsidies, deductions and tax exemptions, need to be strengthened to improve technical innovations, and make renewable energy development more economical and competitive compared to the traditional fossil energy. There is no doubt that the ERM scenario has the best overall effect on reducing cost of resources and environment due to the introduction of the carbon tax. So, to reduce resource consumption and pollution emissions, especially for non-renewable resources, the government needs to levy resource or environmental taxes nationwide as soon as possible. In addition, to consider collection modes and efficiency, it is very important to provide an appropriate explanation for enterprise managers to obtain their supports. In general, the GDM scenario has a positive effect on improving energy and emissions intensity. However, losses of resources and environment caused by rapid economic growth in this scenario are not negligible. Therefore, it is suggested that policies on efficiency improvements are important to save natural resources and avoid over-exploitation. At the same time, it is necessary to upgrade the industrial structure through development of tertiary industries, especially low-carbon industries. Using comparative analysis, the COP scenario is identified as the most appropriate pathway towards GG in Liaoning province. However, energy and CO2 emissions intensity in this scenario needs to be further improved. So the price and financing policies should be reinforced to reduce energy consumption as well as promote pollution treatment and reuse. 6. Conclusions This paper innovatively develops a green growth SD model for Liaoning province in China, which is applied to simulate the prospects of long-term energy consumption, CO2 emissions, and green GDP from 2016 to 2025. Furthermore, five paths for Liaoning’s GG are proposed based on scenario analysis. Finally, an appropriate and effective pathway to achieve GG is identified. The study theoretically establishes a comprehensive SD model better reflecting the real GG system, and practically provides Chinese governors with decision-making guidelines on promoting GG in industrial regions. The simulation results in the BAU, TI, ERM, GDM and COP scenarios show that the total energy consumption and CO2 emissions present a continuing increasing trend during 2016–2025, and energy consumption per GDP and CO2 emissions per GDP show a downward tendency. Additionally, both cost of resources and environment and green GDP per capita reveal increasing trends in all scenarios. The comparison results for the five scenarios show that the energy mix optimization in the COP scenario is more reasonable

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and effective compared to the other four scenarios, where the share of renewable energy consumption increases as the R&D expenditure for exploring and developing new and renewable energy grows. At the same time, in terms of the green GDP per capita, the outcome of the COP scenario is better compared to the others, due to the carbon tax imposed and growth rate increased. Regarding the cost of resources and environment, the COP scenario is ranked second, only above the ERM scenario. Overall, the COP scenario is the most appropriate and effective pathway towards GG, although it is not the “best” one. This study is based on several assumptions, and thus has some limitations. First, there are some factors, such as green industry development, international cooperation and so on, that influence the achievement of GG. But due to data availability, some influencing factors are missed in the original SD model. Accordingly, future research should be directed to build a database for green industries in China, and to collect data to capture industrial characteristics in the implementation process of GG. Another limitation is that both technical innovations and environmental regulations are determined as key factors to promote Liaoning’s GG. There is still the question of whether such suggestions are suitable for other provinces, as provinces differ in their development stages and policy targets. Future work should be done to provide comparative analysis among some Chinese provinces in terms of GG, reinforcing universality of research conclusions in this paper.

Acknowledgments This research is funded by the National Natural Science Foundation of China (NO. 71320107006; NO. 71473029) and the Key Project National Social Science Foundation of China (NO. 14AZD090).

Appendix A. Model validation results Table A1, Fig. A1 The sensitivity test was used to find changes in some relevant variables (e.g., the share of renewable energy consumption, total carbon dioxide emissions, total energy consumption, and green GDP per capita) when the R&D expenditure to GDP increased from 1% to 2%, with the other variables unchanged. The original value of the indicator is 1.5% calculated using historical statistical data from the Liaoning Statistical Yearbook. The results show that the R&D expenditure as a share of the GDP has greater impact on the share of renewable energy consumption, and has major influence on carbon dioxide emissions, and resources and environmental cost. But it has lower influence on the green GDP per capita and energy consumption. The above results provide valuable references for policy adjustments in the system.

Table A1 Historical test results. Year

GDP (billion Yuan)

Error/%

Historical value

Simulated value

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 mean

5033.1 5458.4 6002.6 6671.89 8046.96 9303.9 11163.8 13667.8 15212.2 18457 22225.9 24846.4 27077.6 28626.4 –

5033.1 5458.2 6002.5 6672 8047.3 9304.5 11164.3 13668.6 15212.5 18457.3 22226.7 24846.4 27077.65 28626.6 –

Year

Energy consumption (10000 tce)

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 mean

Historical value

Simulated value

10063.6 10641.1 11312.4 12050.6 13334.2 14307.8 15512.8 16847.4 17562.5 18893.7 20253.8 21124.2 21830.3 22304.8 –

10356.90 10333.50 11430.70 12454.00 12883.30 14228.00 15757.90 16925.70 18172.50 19856.39 21492.07 22313.93 20499.56 20585.67 –

0.000 −0.004 −0.002 0.002 0.004 0.006 0.004 0.006 0.002 0.002 0.004 0.000 0.000 0.001 0.002 Error/%

2.83 −2.98 1.03 3.24 −3.50 −0.56 1.56 0.46 3.36 4.85 5.76 5.33 −6.49 −8.35 0.47

Population (10000 person)

Error/%

Historical value

Simulated value

4147 4155.42 4161.61 4172.8 4189.2 4210.4 4231.71 4246.09 4255.99 4251.69 4255 4244.79 4238 4244.19 –

4147.00 4155.40 4161.60 4172.80 4189.20 4210.40 4231.70 4246.10 4256.00 4251.70 4255.00 4244.80 4238.00 4244.20 –

R&D expenditure (billion Yuan) Historical value

Simulated value

75.4965 81.876 90.039 100.078 120.704 139.559 167.456 205.017 228.184 276.855 333.389 372.695 406.164 429.396 –

53.90 63.70 83.00 106.90 125.30 136.40 166.00 194.20 241.10 287.50 363.80 390.90 445.90 435.20 –

0.0000 −0.0005 −0.0002 0.0000 0.0000 0.0000 −0.0002 0.0002 0.0002 0.0002 0.0000 0.0002 0.0000 0.0002 0.0000 Error/%

−40.07 −28.53 −8.48 6.38 3.67 −2.32 −0.88 −5.57 5.36 3.70 8.36 4.66 8.91 1.33 −3.11

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75%

95%

Current 50%

100%

share of renewable energy consumption 8

75,000

4

50,000

2

25,000

Current 50%

75%

2007

95%

2013 Time (Year)

2019

0 2001

2025

Current 50%

100%

energy consumption 40,000

100%

75%

2007

95%

2013 Time (Year)

2019

2025

2019

2025

100%

green GDP per captica 20

30,000

15

20,000

10

10,000

5

0 2001

95%

carbon dioxide emissions 100,000

6

0 2001

75%

11

2007

2013 Time (Year)

2019

2025

0 2001

2007

2013 Time (Year)

Fig. A1. Sensitivity test results for the share of renewable energy consumption, carbon dioxide emissions, energy consumption, and green GDP per capita.

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Please cite this article in press as: Guo, L.-l., et al., Identifying a pathway towards green growth of Chinese industrial regions based on a system dynamics approach. Resour Conserv Recy (2016), http://dx.doi.org/10.1016/j.resconrec.2016.09.035