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Zhang, F.; Luo, N.; Li, Y. Energy Structure Optimization for Eco-Efficiency. Encyclopedia. Available online: https://encyclopedia.pub/entry/49533 (accessed on 17 November 2024).
Zhang F, Luo N, Li Y. Energy Structure Optimization for Eco-Efficiency. Encyclopedia. Available at: https://encyclopedia.pub/entry/49533. Accessed November 17, 2024.
Zhang, Fan, Nengsheng Luo, Yanfei Li. "Energy Structure Optimization for Eco-Efficiency" Encyclopedia, https://encyclopedia.pub/entry/49533 (accessed November 17, 2024).
Zhang, F., Luo, N., & Li, Y. (2023, September 22). Energy Structure Optimization for Eco-Efficiency. In Encyclopedia. https://encyclopedia.pub/entry/49533
Zhang, Fan, et al. "Energy Structure Optimization for Eco-Efficiency." Encyclopedia. Web. 22 September, 2023.
Energy Structure Optimization for Eco-Efficiency
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This entry is adapted from the peer-reviewed paper 10.3390/su15119130

Increasing environmental pollution, resource depletion, and climate change have led to policymakers paying increased attention to the environmental and ecological impacts of economic activities.

energy structure urban eco-efficiency energy efficiency

1. Introduction

Since the Industrial Revolution in the 18th century, large-scale mechanized production has gradually replaced manual labor, improving the level of productivity and largely satisfying the material needs of human beings. The global acceleration of industrialization and urbanization and therefore of global energy consumption has led to a continuous increase in CO2 emissions. Increasing environmental pollution, resource depletion, and climate change have led to policymakers paying increased attention to the environmental and ecological impacts of economic activities [1]. Schaltegger and Sturm [2] formally defined the connotation of ecological efficiency for the first time, that is, the ratio of economic growth to environmental impact. Specifically, by reducing the consumption of resources and energy in the production process, the impact on the environment can be reduced to meet human needs and improve the quality of life. The core concept of eco-efficiency is “produce more with less input”, that is, obtain more economic benefits with less energy consumption and environmental damage. To comprehensively identify eco-efficiency, Kuosmanen and Kortelainen [3] used the data envelopment analysis (DEA) method in eco-efficiency evaluation for the first time. Rashidi [4] developed the DEA model that divides inputs into energy and non-energy and outputs into ideal (good) and undesirable (bad) outputs and developed a bounded adjustment measurement (BAM) based on green indicators to calculate the eco-efficiency of decision units (DMUs). When multiple economic outputs and environmental impacts are involved, the DEA can give more comprehensive evaluation results compared to the ratio method of a single index, and this has become the mainstream method for evaluating ecological efficiency [5].
This built an ecological efficiency evaluation system, based on the DEA evaluation method. The DEA method was first proposed by Charnes et al. in 1978 [6]. The earliest DEA model was the classical multiplier model, namely CCR-DEA, and its bundle model, which assumed that all decision units were of constant scale efficiency. Banker et al., in 1984, proposed a DEA model based on the variable assumption of scale efficiency, namely the BBC-DEA model, which assumed that the scale of a decision-making unit could not be changed in the short term, and it could improve efficiency using calculation and technical efficiency improvement [7]. With the development of technology and research fields, DEA models were expanded and applied to different scenarios, such as the measurement DEA model based on relaxation [8][9], the efficiency model considering the game relationship between decision units [10], the network DEA model considering the complex production structure [11], the super-efficiency model solving the complete ordering problem [12][13], and a cross-efficiency model based on the “mutual evaluation model” [14]. These DEA models are widely used in different fields, such as technology management [15][16] and medical efficiency evaluation [17]. In addition, the network DEA model based on the network structure model was further developed to describe the inter-period efficiency changes and evaluate the performance [18].
Since the theory of ecological efficiency was introduced in China, the related issues of ecological efficiency have become a topic of concern for the government, enterprises, and scholars. Especially since the reform and opening in the late 1970s, due to China’s development model of pursuing economic scale, environmental crisis has deepened with high emissions and energy consumption that causes severe pollution [19][20][21]. Scholars have studied the impact of energy structure adjustment due to the latest policy reform, termed “energy conservation and emission reduction”. For example, Wu [22] found that the energy structure is highly correlated with CO2 emissions. Zhou [23] proposed that energy carbon emission is the most important contributor to the global greenhouse effect and that the energy structure must be transitioned to achieve low-carbon development. Under the carbon intensity constraint and the increase in the proportion of non-fossil fuel energy, Dong [24] used the dynamic computable general equilibrium model to evaluate the energy-saving and emission reduction effects of all energy and economic sectors during 2012–2030 and concluded that the reduction in carbon emissions was higher than that of energy consumption. Chen [25] studied the ecological efficiency of 30 provinces and cities during 2012–2016 and found that the development level of the ecological economy of China tended to be overestimated when energy structure transition was not taken into account. Moreover, the ecological efficiency in China developed in a U-shaped curve, and the difference in development between provinces and cities showed an increasing trend, followed by a decreasing trend. Yan [26] constructed 3E-DEA models and determined that the performance of 3E targets in various regions of China during 2011–2013 was poor, with a notable difference between the East and West. Some studies have also investigated energy structure and economic development, as well as energy and ecological efficiency. Lin [27] developed an optimization model to determine the optimal energy structure under energy conservation and emission reduction constraints and assessed that a change in the energy structure, mainly coal, would cause an increase in the cost of clean energy inputs and certain negative macro-economic impacts on GDP and employment. Further simulation analysis illustrates that if the government wishes to further reduce emissions, it will need to adjust the energy structure and pay the corresponding energy costs. The emissions constraint decreases from 9.47 billion tons without planning to 8.4 billion tons when coal decreases from 68.7% to 53.2% of the primary energy structure.
In these studies, the issues of the energy structure, energy conservation, emission reduction, and ecological efficiency from different perspectives and involving diversified research methods and data support have been studied extensively, and a considerable amount of valuable research results has been obtained.

2. Energy Structure Optimization for Pollution Emission Reduction and Ecological Efficiency Improvement

With accelerated industrialization and urbanization, global energy consumption (especially traditional fossil fuel energy consumption) sees continuous increase, along with an increase in total CO2 emissions. First, the coal-dominated energy structure significantly affects the overall energy consumption of economic development. According to the National Bureau of Statistics, coal dominates China’s energy consumption, which is not likely to change in the short term. Second, the carbon emission intensity of different energy consumption structures varies substantially, and different energy consumption structures will produce different ecological and environmental effects. The coal-based energy structure will inevitably lead to high carbon emissions and pollutants that consume the same energy, while the gradual decline of fossil fuels (such as coal) in the proportion of resources can effectively reduce greenhouse gas emissions in the production process and improve the level of green production. A decline in the proportion of coal resources can also bring about the extensive use of clean energy sources, effectively decreasing fossil fuel pollution emissions, affecting ecological efficiency. Lin [28] proposed that increasing clean energy consumption contributes to CO2 and SO2 emission reductions, which in turn has a significant positive effect on economic growth. Therefore, different energy structure forms can shape the corresponding overall energy efficiency, and energy structure transition can improve the ecological environment by reducing pollutant emissions, thus affecting the ecological efficiency.

3. Energy Structure Transition for Improvement of Energy Efficiency and Ecological Efficiency

Energy efficiency implies obtaining the same or more effective output with less energy input using technological innovation and improving management. As the country with the highest energy consumption in the world, China exhibits relatively low energy efficiency (Tao [29]; Wang and Xie [30]). Lv and Chen [31] calculated the total factor energy efficiency of 97 countries from 1980–2011, and the results indicated that total factor energy efficiency was relatively low in China and that a regression phenomenon existed due to the negative growth of technological progress and the decline of pure technical efficiency. Policies aiming at energy structure transition and reducing coal and other fossil energy consumption can facilitate or promote the development and utilization of new energy technologies while encouraging energy users to improve energy efficiency and reduce energy consumption. In this way, the energy consumption per unit of economic output can be reduced, while economic output per unit of energy input can be increased and ecological efficiency can be improved.

4. Energy Structure Transition Affects Economic Returns and Ecological Efficiency through Production Cost

The spatial distribution of clean energy in China is heterogeneous with the high price cost of development and consumption [32], so there are significant differences that the impact of energy restructuring in different regions will have on economic growth through the role of cost [33]. First, the economy of the main gas area is at an underdeveloped level, and for regions with strong economic strength and poor clean energy, the promotion of transmission facilities and equipment, technology research, and development investment are required to consume huge amounts of money, while the economic development is relatively lagging behind. In addition, there is the phenomenon of low cost of extraction and inefficient use in regions with relatively abundant resources, so increasing clean energy consumption will increase energy costs and reduce economic efficiency, thus affecting ecological efficiency. Second, industry is dependent on the consumption of fossil energy, and changing the energy structure requires technological innovation and industrial upgrading, which will also entail a greater economic cost. In conclusion, energy restructuring may increase energy costs and economic costs, which will have a negative impact on eco-efficiency. Therefore, the impact of energy restructuring on eco-efficiency depends on the trade-off and comparison between pollution reduction and economic efficiency reduction; when the pollution reduction effect is greater than the efficiency reduction effect, eco-efficiency is effectively improved, and vice versa.
The effect of energy structure on eco-efficiency is bidirectional and nonlinear. As the coal-based energy structure is constantly adjusted in China, clean energy use will reduce energy pollution emissions and promote ecological efficiency; however, it is constrained by the economic and technological development levels. In addition, the great coal energy reduction is not necessarily better. If coal consumption is reduced too rapidly, it will significantly increase energy and economic costs, resulting in a lower pollution reduction effect of the energy structure transition than the economic efficiency reduction effect, thus reducing the ecological efficiency.

5. Energy Structure Has a Spatial Spillover Effect on Eco-Efficiency

Through pollutant emission and energy efficiency improvement, energy structure has a spatial spillover and spatial interaction effect on environmental pollution in surrounding cities. In the energy structure transition process, pollutant discharge has a natural fluidity, which has impacted the previous governmental concept of “only sweeping your own snow”. Both the energy structure and environmental pollution have spatial spillover effects. In the initial stage, each city will reduce pollutant emissions by transitioning and optimizing the energy structure. With clean energy use, the ecological environment of the region will be further optimized. However, inertia of the energy structure transition may also be generated, which directly leads to the beggar-thy-neighbor phenomenon of environmental pollution. Further, it is also expected that neighboring regions can optimize pollutant emissions in the whole region through energy structure transition. In contrast, there may be spatial interactions between the energy structure and environmental pollution, that is, local energy structure transition can promote the surrounding cities to carry out energy structure transition through competition, learning, and radiation driving effects, increase the scale of clean energy use and the investment in energy use technology, and improve energy efficiency and the ecological environment. The improvement of local ecological environment is accompanied by high-level environmental governance ability, technological innovation ability, and economic development strength, so as to attract industrial agglomeration, talent, and capital inflow and further promote economic development and improve the economic efficiency of energy structure transition.

References

  1. Lansink, A.O.; Wall, A. Frontier models for evaluating environmental efficiency: An overview. Econ. Bus. Lett. 2014, 3, 43–50.
  2. Schaltegger, S.; Sturm, A. Okologische rationalitatan-satzpunkte-zur ausgestalung von okologieorientierten management instrumenten. Untemehmung 1990, 4, 273–290.
  3. Kuosmanen, T.; Kortelainen, M. Measuring eco-efficiency of production with data envelopment analsis. J. Ind. Ecol. 2005, 9, 59–72.
  4. Rashidi, K.; Saen, R.F. Measuring eco-efficiency based on green indicators and potentials in energy saving and undesirable output abatement. Energy Econ. 2015, 50, 18–26.
  5. Yang, L.; Zhang, X. Assessing regional eco-efficiency from the perspective of resource, environmental and economic performance in China: A bootstrapping approach in global data envelopment analysis. J. Clean. Prod. 2018, 173, 100–111.
  6. Charnes, A.; Cooper, W.W.; Rhodes, E. Measuring the efficiency of decision making units. Eur. J. Oper. Res. 1978, 2, 429–444.
  7. Banker, R.D.; Charnes, A.; Cooper, W.W. Some models for estimating technical and scale inefficiencies in DEA. J. Manag. Sci. 1984, 30, 1078–1092.
  8. Zuo, Z.M.; Yang, L. Analysis of total factor energy efficiency in China: Based on SBM model. Stat. Decis. 2011, 20, 105–107.
  9. Avkiran, N.K.; Tone, K.; Tsutsui, M. Bridging radial and non-radial measures of efficiency in DEA. Ann. Oper. Res. 2008, 164, 127–138.
  10. Wu, J.; Liang, L. A multiple criteria ranking method based on game cross-evaluation approach. Ann. Oper. Res. 2012, 197, 191–200.
  11. Moreno, P.; Lozano, S. A network DEA assessment of team efficiency in the NBA. Ann. Oper. Res. 2014, 214, 99–124.
  12. Kuang, H.B.; Chen, S. Investigating cost efficiency of China port public companies based on SUP-CCR-DEA. J. Chin. Manag. 2007, 15, 142–148.
  13. Avkiran, N.K.; Cai, L. Identifying distress among banks prior to a major crisis using non-oriented super-SBM. Ann. Oper. Res. 2014, 217, 31–53.
  14. Yu, Y.; Zhu, W.; Zhang, Q. DEA cross-efficiency evaluation and ranking method based on interval data. Ann. Oper. Res. 2019, 278, 159–175.
  15. Song, M.L.; Wang, S.H. Environmental regulation, technological progress and economic growth. Econ. Res. 2013, 48, 122–134.
  16. Song, M.; Tao, J.; Wang, S. FDI, technology spillovers and green innovation in China: Analysis based on Data Envelopment Analysis. Ann. Oper. Res. 2015, 228, 47–64.
  17. Fragkiadakis, G.; Doumpos, M.; Zopounidis, C.; Germain, C. Operational and economic efficiency analysis of public hospitals in Greece. Ann. Oper. Res. 2014, 247, 787–806.
  18. Petridis, K.; Chatzigeorgiou, A.; Stiakakis, E. A spatiotemporal Data Envelopment Analysis (S-T DEA) approach: The need to assess evolving units. Ann. Oper. Res. 2016, 238, 475–496.
  19. Chen, H.; Wu, Q.Y.; Cheng, J.Q.; Ma, Z.; Song, W. Scaling-up strategy as an appropriate approach for sustainable new town development? Lessons from Wujin, Changzhou, China. Sustainability 2015, 7, 5682–5704.
  20. Wu, Q.Y.; Zhang, X.L.; Shang, Z.Y.; Li, Z. Political-economy based institutional industry complex and sustainable development: The case of the salt-chemical industry in Huai’an, China. Energy Policy 2015, 87, 39–47.
  21. Li, Z.J.; Yao, Y.X.; Ma, Z.F.; Hu, M.J.; Wu, Q.Y. Spatial pattern of ecological efficiency in China and its influencing factors. J. Environ. Sci. 2016, 36, 4208–4217.
  22. Wu, B.A.; Gao, J.H.; Xu, C. The decomposition analysis of carbon emission in Henan Province based on the industrial structure and energy structure. Econ. Geogr. 2010, 30, 1902–1907.
  23. Zhou, Y.N.; He, Z.; Ma, L.; Yang, Y.; Zhang, T.Y.; Chen, L.Y. Spatial and temporal differentiation of China’s provincial scale energy consumption structure. Resour. Sci. 2017, 39, 2247–2257.
  24. Dong, M.; Xu, Z.Y.; Li, C.F. The impact of carbon intensity restriction on welfare of urban and rural residents: An analysis based on CGE Model. China’s Popul. Resour. Environ. 2018, 28, 94–105.
  25. Chen, Y.W.; Wang, M.Q.; Li, D. Study on China’s Inter-provincial Eco-efficiency Based on Improved SBM Model. Sci. Technol. Manag. Res. 2019, 39, 7.
  26. Yan, Q.; Wang, X.; Baležentis, T.; Streimikiene, D. Energy–economy–environmental (3E) performance of Chinese regions based on the data envelopment analysis model with mixed assumptions on disposability. Energy Environ. 2018, 29, 664–684.
  27. Lin, B.Q.; Yao, X.; Liu, X.Y. China’s energy strategy adjustment under energy conservation and carbon emission constraints. Soc. Sci. China 2010, 1, 58–71.
  28. Lin, M.S. The clean energy consumption, environment governance and the sustainable economic growth in China. China J. Quant. Tech. Econ. 2017, 12, 3–21.
  29. Tao, X.P.; Wang, P.; Zhu, B.Z. Energy efficiency in APEC: A meta-frontier SBM-undesirable approach. China J. Beijing Inst. Technol. (Soc. Sci. Ed.) 2015, 17, 20–28.
  30. Wang, B.; Xie, J. International comparison of total factor energy efficiency—An empirical research from the perspective of energy structure. China J. Ind. Econ. Rev. 2015, 6, 72–86.
  31. Lv, B.B.; Chen, S.J. International comparison of total factor energy efficiency—An empirical study based on Malmquist index. Manag. Admin. 2015, 7, 71–74.
  32. Xu, W.X.; Xu, Z.X.; Liu, C.J. Energy structure, ecological environment and economic development: Analysis of threshold effect and heterogeneity. Stat. Inf. 2020, 10, 81–89.
  33. Xu, B.; Chen, Y.F.; Shen, X.B. Clean energy development, carbon dioxide emission reduction and regional economic growth. China Econ. Res. J. 2019, 7, 188–202.
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