:::

詳目顯示

回上一頁
題名:氣候變遷對中國能源影響及最適配置之研究
作者:黃桂英
作者(外文):HUANG, KUEI-YING
校院名稱:東吳大學
系所名稱:經濟學系
指導教授:邱永和
學位類別:博士
出版日期:2022
主題關鍵詞:EBM模型動態方向距離函數(DDF)模型零和賽局(ZSG)方向距離函數(DDF)二階段共同邊界外生氣候變遷最適配置非意欲產出Epsilon-Based Measure (EBM) ModelDynamic Directional Distance Function (DDF) ModelZero-Sum Gain(ZSG) DDFTwo-stage Meta FrontierExogenous climate changeOptimal allocationUndesirable output
原始連結:連回原系統網址new window
相關次數:
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:3
地球暖化的嚴重問題,近來已經引起各方重視,不只各國政府重視,包括科技巨頭們,雖然被環境專家質疑「科技減碳夢的力氣沒放在對的地方」,但仍然紛紛提出很多減碳計畫。因此,氣候或全球暖化的問題,已經從過去的感性訴求,逐漸進入到現今的理性數據比較。
本研究將過去大家所忽略的最高氣溫與最低氣溫之天數變化情形及降水量狀況,特別納入模型考量作為外生變數,並分別於第二章至第四章專題分析三大主題,評估全球最大碳排放國家(即中國)各省份的電力與能源之效率表現暨新舊能源與二氧化碳(CO2)之最適配置,透過氣候變遷作為外生變數,協助我們解讀效率影響情況,更盤點中國各省份新能源與舊能源的生產現況及二氧化碳(CO2)排放情形,透過本研究特別建構並首創的專屬模型,以掌握正確的改善空間,並提出具體的政策規劃建議。
本研究第二章係採用具有外生變數及非意欲產出的兩階段共同邊界EBM模型(Two-stage meta frontier EBM model with exogenous variable and undesirable outputs),在極端氣候「高低溫天數」影響下,以就業人數、電力消費、產業固定資產為投入,國民所得(GDP)為好的產出,二氧化碳(CO2)、直徑小於或等於2.5微米的懸浮微粒(PM2.5)為壞產出,並以區域因素劃分為東中部和西部,評估2013年至2017年氣候變遷對中國電力消費、空氣污染及所得效率之變化及其差異性。評估結果證實中國仍然過度傾斜地追求經濟成長的果實,嚴重忽視環境責任問題,並且幾乎可斷定PM2.5的管制政策出現嚴重破口,尤其西部地區有極大的改善空間,中國政府有必要全面檢討電力監管政策,並發現氣候危機係無邊界狀態,需由中國所有省份共同集體對抗。
本研究第三章係採用具有外生變數及非意欲產出的動態兩階段共同邊界之方向距離函數模型(Dynamic two-stage meta frontier DDF model with exogenous variable and undesirable outputs),在極端氣候「高低溫天數」影響下,以就業人口、能源消費、資本存量為投入,國民所得(GDP)為好的產出,二氧化碳(CO2)、直徑小於或等於2.5微米的懸浮微粒(PM2.5)為壞產出,其中資本存量也是動態跨期間(carry over)變數,並以區域因素劃分為東中部和西部,評估2014年至2017年氣候變遷對中國能源消費和環境污染的影響。評估結果證實中國能源消費效率低落,因此中國應該謹慎制定官方零碳排放長期計畫,尤其必須重新盤點西部地區的能源政策,也必須運用國家政策支持再生能源投資計畫,必須透過科技突破及查證特殊原因並導入氣候影響揭露,減少能源無效使用。
本研究第四章係採用具有外生變數及非意欲產出的動態零和賽局方向距離函數模型(Dynamic ZSG-DDF model with exogenous variable and undesirable outputs),在極端氣候「降水量多寡」影響下,以就業人口、資本存量及新舊不同能源作為投入項,並設定國民所得(GDP)為好的產出,但二氧化碳(CO2)、二氧化硫(SO2)、二氧化氮(NO2)為三個壞產出變數,在「單位國民所得(GDP)二氧化碳排放」降低18%及「可再生能源在一次能源消費增量中的比重」超過50%的中國「十四五」政策目標下,並假設國民所得(GDP)不變的情況,不同省份之間全部都維持效率極大化的前提下,重新分配二氧化碳、新能源、舊能源的各省份最適配置。評估結果證實中國仍較偏重經濟成長及舊能源發展,其中新能源效率更是嚴重低落,因此能源政策及二氧化碳等污染面政策都必須重新調整方向。
本論文研究證實中國西部地區的技術落差比例、共同邊界效率、各項變數的分項效率下之技術缺口受氣溫的影響程度,確實高於東中部地區;中國政府對於各省份的新能源、舊能源、二氧化碳的配額嚴重錯置,導致無效率現象。
但因為受限於研究時間及資料,目前尚無充分證據證明氣候變遷對各項變數在分項效率下的技術缺口影響趨勢,希望後續學者可以做其他更深入的統計檢定,繼續保持嚴謹態度並接棒持續研究「氣候變遷對於能源效率更多面向的探討」,例如:可以考慮氣候變遷對於能源各部門的作業影響情形,衡量網路系統效率,儘可能找出系統與部門效率之間明確的數學關係,將更有助於電力與能源後續系統效率之改進,才能避免只考慮系統之整體行為而忽略內部各部門之交互影響情形,造成黑箱模式之效率評估作業。
最後,考量碳權只是一個許可權,已不再是如同實體經濟所涉及的各項看得見的實體程序(例如:運送、通關、報稅)牽制的傳統經濟行為,因此法規面若不完備,將造成犯罪漏洞,也會造成不效率的市場運作機制,因此本文也提出碳權交易市場可作為未來的研究建議,尤其建議可以先針對2009年前後的歐盟碳市場交易作研究分析,因為歐盟碳市場曾經於2009年一度因為一連串詐騙案而緊急停止交易,藉此可分析其改革前後的效率變化,以作為中國已正式啟動全國碳交易後的政策調整參考方向,亦可作為臺灣及其他尚未實施碳交易市場國家的未來施政重要參考之一。
The serious global warming problem has not only attracted the attention of governments of various countries but also attracted the response of leading technology leaders, although experts questioned that "the dream of reducing carbon emissions by technology is not placed in the right place" but many carbon reduction plans have still been proposed. Therefore, the problem of global warming has gradually entered the comparison of rational empirical data from the perceptual appeals in the past.
In this study, the past literature ignores the changing days of the highest and lowest temperature and rainfall and incorporates it into model considerations as exogenous variables. Based on the analysis of three themes and the research content in Chapters 2 to 4, the efficiency performance of electrical energy and the optimal allocation of new and old energy and carbon dioxide (CO2) in various provinces in China are evaluated. Climate change is used as an exogenous variable to evaluate and analyze the situation that affects efficiency. To put forward specific policy plans and recommendations, the study counted the current production status of new and old energy and carbon emissions and mastered the DMU (Decision-making unit) the room for improvement through a specially constructed and innovative exclusive model.
Chapter 2 considers "high and low-temperature days" as an exogenous variable and uses the Two-stage meta frontier EBM model to incorporate the concept of undesirable output to explore the impact of extreme climates. To assess the changes and differences of climate change on China's power consumption, air pollution, and income efficiency from 2013 to 2017. In the study, the eastern, central, and western regions were divided by regional factors, and the number of employees, electricity consumption and industrial fixed assets were used as input variables. GDP was the desirable output variable, and carbon dioxide (CO2) and PM2.5 were undesirable output variables. The empirical results confirm that China is overly pursuing economic growth, ignoring PM2.5 environmental issues, and serious breaches in regulatory policies, especially in the western region, where there is great room for improvement. It is necessary for the Chinese government to comprehensively review power regulatory policies and collectively combat the climate crisis.
Chapter 3 is based on "the number of days with extreme temperature changes" as the exogenous variable, using the Dynamic two-stage meta frontier DDF model, where investment is the inter-temporal carry-over variable, to evaluate the effects of climate change on energy consumption and environmental pollution in the eastern, central, and western regions of China from 2014 to 2017. The evaluation results confirmed that China’s energy consumption efficiency is low, and Chinese officials should carefully formulate a long-term plan for net-zero carbon emissions. In particular, the energy policy of the western region must start from the national policy to support renewable energy investment plans, through technological breakthroughs and verification of emission sources, and the development of an information system that exposes the impact of climate to reduce inefficient energy consumption.
Chapter 4 employed the rainfall of climate change as an exogenous variable and assuming that all provinces in China maintain maximum efficiency, to adopt the Dynamic ZSG-DDF model to explore China’s 14th Five-Year Plan that reducing CO2 emissions by 18% and 50% renewable energy increase in primary energy consumption, the optimal allocation of CO2, new energy and traditional energy in various provinces of China. The results confirmed that China still places more emphasis on economic growth and traditional energy development, and the performance of new energy efficiency is severely low. Regarding renewable energy policies and carbon emission policies, the government needs to pay more attention to the direction of future development.
The research in this paper confirms that the technology gap, Meta frontier efficiency, and factor efficiencies in the western region of China are affected by the temperature, which is indeed higher than those in the eastern and central regions; The new energy, old energy, and carbon dioxide in China's provinces are in a state of inefficiency that is not optimally allocated.
As the limitation of research data, there is currently no sufficient evidence to prove the trend of climate change's impact on factors efficiency. Follow-up scholars can study more in-depth discussions of climate change on energy efficiency. Such as the effects of climate change on energy use in various sectors, finding out the relationship between system efficiency and inter-sectoral efficiency, and improving energy system efficiency. Avoid the interaction of internal departments' efficiency evaluation in the black box.
Finally, carbon rights are not traditional behavioral procedures involved in the real economy (such as transportation, customs clearance, and tax payment). Therefore, imperfect regulations will cause legal loopholes and inefficient market mechanisms. Therefore, this research proposes to conduct market research on future carbon rights trading, aiming at the EU carbon market before and after the 2009 suspension of trading due to fraud cases, and conduct an efficiency analysis after market reforms. China has officially launched national carbon trading as a reference direction for policy adjustments, and it can also be used as an important reference for the governance of Taiwan and other markets that have not yet implemented carbon trading.
1.Abbasian, M. S., Mohammad, R.N.,& Abrishamchic, A.(2021). Increasing risk of meteorological drought in the Lake Urmia basin under climate change: Introducing the precipitation–temperature deciles index. Journal of Hydrology,592,125586.
2.Agarwal, S., Sing, T. F., & Yang, Y. (2020). The impact of transboundary haze pollution on household utilities consumption. Energy Economics, 85, 104591.
3.Ahmadi, S., Fakehi, A. H., Vakili, A., & Moeini-Aghtaie, M. (2020). An optimization model for the long-term energy planning based on useful energy, economic and environmental pollution reduction in residential sector: A case of Iran. Journal of Building Engineering, 30, 101247.
4.Akadiri, S. S., Alola, A. A., Akadiri, A. C., & Alola, U. V. (2019). Renewable energy consumption in EU-28 countries: policy toward pollution mitigation and economic sustainability. Energy Policy, 132, 803-810.
5.Akadiri, S. S., Alola, A. A., Olasehinde-Williams, G., & Etokakpan, M. U. (2020). The role of electricity consumption, globalization and economic growth in carbon dioxide emissions and its implications for environmental sustainability targets. Science of The Total Environment, 708, 134653.
6.Akbari, N., Jones, D., & Treloar, R. (2020). A Cross-European Efficiency Assessment of Offshore Wind Farms: a DEA Approach. Renewable Energy, 151, 1186-1195.
7.Apergis, N., Payne, J. E., Menyah, K., & Wolde-Rufael, Y. (2010). On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth. Ecological Economics, 69(11), 2255–2260.
8.Asongu, S. A., Agboola, M. O., Alola, A. A., & Bekun, F. V. (2019). Renewable and non-renewable electricity consumption, environmental degradation and economic development: Evidence from Mediterranean countries. Energy Policy, 133, 110929.
9.Asongu, S. A., Agboola, M. O., Alola, A. A., & Bekun, F. V. (2020). The criticality of growth, urbanization, electricity and fossil fuel consumption to environment sustainability in Africa. Science of the Total Environment, 712, 136376.
10.Assi, A. F., Isiksal, A. Z., & Tursoy, T. (2021). Renewable energy consumption, financial development, environmental pollution, and innovations in the ASEAN+ 3 group: Evidence from (P-ARDL) model. Renewable Energy, 165, 689-700.
11.Awodumi, O. B., & Adewuyi, A. O. (2020). The role of non-renewable energy consumption in economic growth and carbon emission: Evidence from oil producing economies in Africa. Energy Strategy Reviews, 27, 100434.
12.Bampatsou, C., Papadopoulos, S., & Zervas, E. (2013). Technical efficiency of economic systems of EU-15 countries based on energy consumption. Energy Policy, 55, 426–434.
13.Bastola, U., & Sapkota, P. (2015). Relationships among energy consumption, pollution emission, and economic growth in Nepal. Energy, 80, 254-262.
14.Ben Mbarek, M., Saidi, K., & Amamri, M. (2018). The relationship between pollutant emissions, renewable energy, nuclear energy and GDP: empirical evidence from 18 developed and developing countries. International Journal of Sustainable Energy, 7(6), 597–615.
15.Bi, G.-B., Song, W., Zhou, P., & Liang, L.(2014). Does environmental regulation affect energy efficiency in China's thermal power generation? Empirical evidence from a slacks-based DEA model. Energy Policy, 66, 537–546.
16.Bildirici, M. E., & Gökmenoğlu, S. M. (2017). Environmental pollution, hydropower energy consumption and economic growth: Evidence from G7 countries. Renewable and Sustainable Energy Reviews, 75, 68-85.
17.Boubaker, K. (2012). Renewable energy in upper North Africa: Present versus 2025-horizon perspectives optimization using a Data Envelopment Analysis (DEA) framework. Renewable Energy, 43, 364–369.
18.Boukal, D. S., Bideault, A., Carreira, B. M.,& Sentis, A.(2019). Species interactions under climate change: connecting kinetic effects of temperature on individuals to community dynamics. Current Opinion in Insect Science,35,88-95.
19.Boukhelkhal, A., & Bengana, I. (2018). Cointegration and causality among electricity consumption, economic, climatic and environmental factors: Evidence from North-Africa region. Energy, 163, 1193-1206.
20.Brini, R. (2021). Renewable and non-renewable electricity consumption, economic growth and climate change: Evidence from a panel of selected African countries. Energy, 223, 120064.
21.Carvalho, K.S.,& Wang, S.(2020). Sea surface temperature variability in the Arctic Ocean and its marginal seas in a changing climate: Patterns and mechanisms. Global and Planetary Change,193,103265.
22.Chakamera, C., & Alagidede, P.(2018). Electricity crisis and the effect of CO2 emissions on infrastructure-growth nexus in Sub Saharan Africa.Renewable and Sustainable Energy Reviews, 94,945-958.
23.Chang, M.-C., Hu, J.-L., & Chen, C.-H.(2019).A metafrontier pollution efficiency analysis of Taiwan’s administrative Regions. Journal of Cleaner Production, 222, 393-406.
24.Charnes, A., Cooper, W.W., & Rhodes, E.(1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6),429-444.
25.Chen, P.-C., Yu, M.-M., Chang, C.-C., Hsu, S.-H., & Managi, S.(2015). The enhanced Russell-based directional distance measure with undesirable outputs: Numerical example considering CO2 emissions. Omega, 53, 30-40.
26.Chen, X., Fu, Q., & Chang, C.-P. (2021). What are the shocks of climate change on clean energy investment: A diversified exploration. Energy Economics, 95, 105136.
27.Chiu, Y.-H., Huang, K.-Y., Chang, T.-H., & Lin, T.-Y. (2021). Efficiency assessment of coal mine use and land restoration : Considering climate change and income differences. Resources Policy,73,102130.
28.Chiu, Y. H., Lin, J. C., Hsu, C. C., & Lee, J. W. (2013). Carbon Emission Allowances of Efficiency Analysis: Application of Super SBM ZSG-DEA Model. Polish Journal of Environmental Studies, 22(3).
29.Chiu, Y. H., Lin, J. C., Su, W. N., & Liu, J. K. (2015). An efficiency evaluation of the EU’s allocation of carbon emission allowances. Energy Sources, Part B: Economics, Planning, and Policy, 10(2), 192-200.
30.Chung, Y. H., Fa¨re, R., & Grosskopf, S.(1997). Productivity and Undesirable Outputs: A Directional Distance Function Approach. Journal of Environmental Management, 51(3), 229–240.
31.Cooper, W. W., Ruiz, J. L., & Sirvent, I. (2007). Choosing weights from alternative optimal solutions of dual multiplier models in DEA. European Journal of Operational Research, 180(1), 443-458.
32.Cui, X., Zhao, T., & Wang, J. (2021). Allocation of carbon emission quotas in China’s provincial power sector based on entropy method and ZSG-DEA. Journal of Cleaner Production, 284, 124683.
33.Cui,X., Zhao,T., & Wang, J. (2021). Allocation of carbon emission quotas in China’s provincial power sector based on entropy method and ZSG-DEA. Journal of Cleaner Production, 284(15),124683.
34.Dagnachew, A. G., Lucas, P. L., Hof, A. F., & Vuuren, D. P. V. (2018). Trade-offs and synergies between universal electricity access and climate change mitigation in Sub-Saharan Africa. Energy Policy, 114, 355-366.
35.Des, M., Fernández-Nóvoa, D., deCastro, M., Gómez-Gesteira, J.L., Sousa, M.C., & Gómez-Gesteira, M.(2021). Modeling salinity drop in estuarine areas under extreme precipitation events within a context of climate change: Effect on bivalve mortality in Galician Rías Baixas. Science of The Total Environment,790,148147.
36.Ding, L., Yang, Y., Wang, W., & Calin, A. C.(2019). Regional carbon emission efficiency and its dynamic evolution in China: A novel cross efficiency-malmquist productivity index. Journal of Cleaner Production, 241, 118260.
37.Exley, G., Armstrong, A., Page, T.,& Jones, I. D.(2021). Floating photovoltaics could mitigate climate change impacts on water body temperature and stratification. Solar Energy,219,24-33.
38.Fan, J.-L., Hu, J.-W., & Zhang, X. (2019). Impacts of climate change on electricity demand in China: An empirical estimation based on panel data. Energy, 170, 880-888.
39.Fang, K., Zhang, Q., Long, Y., Yoshida, Y., Sun, L., Zhang, H., & Li, S. (2019). How can China achieve its Intended Nationally Determined Contributions by 2030? A multi-criteria allocation of China’s carbon emission allowance. Applied energy, 241, 380-389.
40.Färe, R., & Grosskopf, S.(2010). Directional Distance Functions and Slacks-Based Measures of Efficiency. European Journal of Operational Research, 200(1), 320-322.
41.Färe, R., & Grosskopf, S. (1996). Productivity and intermediate products: a frontier approach Economics Letters, 50, 65-70.
42.Färe, R., Grosskopf, S., Norris, S., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84(1), 66–83.
43.Ge, F., Ye, B., Xing, S., Wang, B., & Sun, S. (2017). The analysis of the underlying reasons of the inconsistent relationship between economic growth and the consumption of electricity in China–A case study of Anhui province. Energy, 128, 601-608.
44.Ghazouani, T., Boukhatem, J., & Sam, C. Y. (2020). Causal interactions between trade openness, renewable electricity consumption, and economic growth in Asia-Pacific countries: Fresh evidence from a bootstrap ARDL approach. Renewable and Sustainable Energy Reviews, 133, 110094.
45.Gomes, E. G., & Lins, M. P. E.(2008). Modelling undesirable outputs with zero sum gains data envelopment analysis models. Journal of the Operational Research Society. 59, 616-623.
46.Gregori, T., & Tiwari, A. K. (2020). Do urbanization, income, and trade affect electricity consumption across Chinese provinces? Energy Economics, 89,104800.
47.Hernández-Sancho, F., Molinos-Senante, M., & Sala-Garrido, R.(2011). Energy efficiency in Spanish wastewater treatment plants: A non-radial DEA approach. Science of the Total Environment, 409(14), 2693-2699.
48.Honma, S., & Hu, J.-L.(2018). A meta‑stochastic frontier analysis for energy efficiency of regions in Japan. Journal of Ecomic Structures, 7(21).
49.Hosseinzadehtalaei, P., Tabari, H.,& Willems, P.(2020). Climate change impact on short-duration extreme precipitation and intensity–duration–frequency curves over Europe. Journal of Hydrology,590,125249.
50.Hu, J.-L., & Wang, S.-C.(2006). Total-factor energy efficiency of regions in China. Energy Policy, 34, 3206-3217.
51.Hu, J.-L., & Chang, T.-P.(2016a). Total-factor energy efficiency and its extensions: introduction, computation and application. International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, chapter 0, pages 45-69, Springer.
52.Hu, J.-L., & Chang, T.-P.(2016b). Energy and pollution efficiencies in China’s regions. China's Energy Efficiency and Conservation, 61-74.
53.Huang, C. J., Huang, T. H., & Liu, N. H. (2014). A new approach to estimating the metafrontier production function based on a stochastic frontier framework. Journal of Productivity Analysis, 42:241-254.
54.Huang, H., Patricola, C. M., Winter, J. M., Osterberg, E. C.,& Mankin, J. S.(2021). Rise in Northeast US extreme precipitation caused by Atlantic variability and climate change. Weather and Climate Extremes,33,100351.
55.International Energy Agency. (2004). World Energy Outlook 2004.
56.Islam, M. R., Mekhilef, S., & Saidur, R. (2013). Progress and recent trends of wind energy technology. Renewable and Sustainable Energy Reviews, 21, 456–468.
57.Jafarpur, P.,& Berardi, U.(2021). Effects of climate changes on building energy demand and thermal comfort in Canadian office buildings adopting different temperature setpoints. Journal of Building Engineering,42,102725.
58.Jiang, P., Khishgee, S., Alimujiang, A., & Dong, H. (2020). Cost-effective approaches for reducing carbon and air pollution emissions in the power industry in China. Journal of Environmental Management, 264, 110452.
59.Jun, W., Mughal, N., Zhao, J., Shabbir, M. S., Niedbała, G., Jain, V., & Anwar, A. (2021). Does globalization matter for environmental degradation? Nexus among energy consumption, economic growth, and carbon dioxide emission. Energy Policy, 153, 112230.
60.Kahia, M., Aïssa, M. S. B., & Lanouar, C. (2017). Renewable and non-renewable energy use - economic growth nexus: The case of MENA Net Oil Importing Countries. Renewable and Sustainable Energy Reviews, 71, 127–140.
61.Kaivo-oja, J., Vehmas, J., & Luukkanen, J. (2016). Trend analysis of energy and climate policy environment: Comparative electricity production and consumption benchmark analyses of China, Euro area, European Union, and United States. Renewable and Sustainable Energy Reviews, 60, 464-474.
62.Karakosta, C., Pappas, C., Marinakis, V., & Psarras, J. (2013). Renewable energy and nuclear power towards sustainable development: Characteristics and prospects. Renewable and Sustainable Energy Reviews, 22, 187–197.
63.Kim, K.-T., Lee, D. J., Park, S.-J., Zhang, Y., & Sultanov, A. (2015). Measuring the efficiency of the investment for renewable energy in Korea using data envelopment analysis. Renewable and Sustainable Energy Reviews, 47, 694–702.
64.Klopp, G. A. (1985). The analysis of the efficiency of productive systems with multiple inputs and outputs (Doctoral dissertation, University of Illinois at Chicago).
65.Kwon, D. S., Cho, J. H., & Sohn, S. Y. (2017). Comparison of technology efficiency for CO 2 emissions reduction among European countries based on DEA with decomposed factors. Journal of Cleaner Production, 151, 109–120.
66.Lawal, A. I., Ozturk, I., Olanipekun, I. O., & Asaleye, A. J. (2020). Examining the linkages between electricity consumption and economic growth in African economies. Energy, 208,118363.
67.Li, K., & Lin, B. (2015). Metafroniter energy efficiency with CO2 emissions and its convergence analysis for China. Energy Economics, 48, 230-241.
68.Li, L., Hong, X., & Wang, J. (2020). Evaluating the impact of clean energy consumption and factor allocation on China’s air pollution: A spatial econometric approach. Energy, 195,116842.
69.Li, X.-X. (2018). Linking residential electricity consumption and outdoor climate in a tropical city. Energy, 157, 734-743.
70.Li, Y., Chiu, Y.-H., Wang, L., Zhou, Y., & Lin, T.-Y. (2020). Dynamic and network slack-based measure analysis of China’s regional energy and air pollution reduction efficiencies. Journal of Cleaner Production, 251, 119546.
71.Li, Y., Chiu, Y.-H., & Lu, L. C. (2019). New Energy Development and Pollution Emissions in China. International Journal of Environmental Research and Public Health, 16(10), 1764.
72.Li, Y., Chiu, Y.-h. & Lin, T.-Y. (2019). Energy and environmental efficiency in different Chinese regions. Sustainability,11(4), 1216.
73.Li, Y., Chiu, Y.-h., & Lu, L. C.(2019). Energy, CO2, AQI and economic performance in 31 cities in China: a slacks-based dynamic data envelopment analysis. Journal Carbon Management, 10(3), 269-286.
74.Li, Y., Chiu, Y.-h., Wang, L., Liu, Y.-C., & Chiu, C.-R.(2019). A Comparative Study of Different Energy Efficiency of OECD and Non-OECD Countries. Tropical Conservation Science.
75.Lin, B., & Du, K. (2013). Technology gap and China's regional energy efficiency: a parametric metafrontier approach. Energy Economics, 40, 529-536.
76.Lin, B., & Wang, Y. (2019). Inconsistency of economic growth and electricity consumption in China: A panel VAR approach. Journal of Cleaner Production, 229, 144-156.
77.Lins, M. P. E., Gomes, E. G., de Mello, J. C. C. S., & de Mello, A. J. R. S. (2003). Olympic ranking based on a zero sum gains DEA model. European Journal of Operational Research, 148(2), 312-322.
78.Liu, D., Ruan, L., Liu, J., Huan, H., Zhang, G., Feng, Y., & Li, Y. (2018). Electricity consumption and economic growth nexus in Beijing: A causal analysis of quarterly sectoral data. Renewable and Sustainable Energy Reviews, 82, 2498-2503.
79.Liu, T., Zheng, Z., & Du, Y. (2020). Evaluation on regional science and technology resources allocation in China based on the zero sum gains data envelopment analysis. Journal of Intelligent Manufacturing, 1-9.
80.Liu, X., Sun, T., Feng, Q., & Zhang, D. (2020). Dynamic nonlinear influence of urbanization on China’s electricity consumption: Evidence from dynamic economic growth threshold effect. Energy, 196, 117187.
81.Mahpour, A.,& El-Diraby, T.(2021). Incorporating Climate Change in Pavement Maintenance Policies: Application to Temperature Rise in the Isfahan County, Iran. Sustainable Cities and Society,71,102960.
82.Mbarek, M. B., Khairallah, R., & Feki, R. (2015). Causality relationships between renewable energy, nuclear energy and economic growth in France. Environment Systems and Decisions, 35, 133–142.
83.Mei, H., Li, Y. P., Suo, C., Ma, Y., & Lv, J. (2020). Analyzing the impact of climate change on energy-economy-carbon nexus system in China. Applied Energy, 262, 114568.
84.Meng, F., Su, B., Thomson, E., Zhou, D., & Zhou, P.(2016). Measuring China’s regional energy and carbon emission efficiency with DEA models: A survey. Applied Energy, 183(1), 1-21.
85.Meng, M., Jing, K., & Mander, S.(2017). Scenario analysis of CO2 emissions from China's electric power industry. Journal of Cleaner Production, 142(4), 3101-3108.
86.Mohan, S., Mishra, S. K., Sahany, S.,& Behera, S.(2021). Long-term variability of Sea Surface Temperature in the Tropical Indian Ocean in relation to climate change and variability. Global and Planetary Change,199,103436.
87.Mohsin, M., Kamran, H. W., Nawaz, M. A., Hussain, M. S., & Dahri, A. S. (2021). Assessing the impact of transition from nonrenewable to renewable energy consumption on economic growth-environmental nexus from developing Asian economies. Journal of environmental management, 284, 111999.
88.Morid, R., Shimatani, Y.,& Sato, T.(2020). An integrated framework for prediction of climate change impact on habitat suitability of a river in terms of water temperature, hydrological and hydraulic parameters. Journal of Hydrology,587,124936.
89.Mumenthaler, C., Renaud, O., Gava, R., & Brosch, T.(2021). The impact of local temperature volatility on attention to climate change: Evidence from Spanish tweets. Global Environmental Change,69,102286.
90.Nepal, R., & Paija, N. (2019). Energy security, electricity, population and economic growth: The case of a developing South Asian resource-rich economy. Energy policy, 132, 771-781.
91.O’Donnell, C .J., Rao, D. S. P., & Battese, G. E.(2007). Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical Economics, 34, 231-255.
92.Oka, K., Mizutani, W., & Ashina, S. (2020). Climate change impacts on potential solar energy production: A study case in Fukushima, Japan. Renewable Energy, 153, 249-260.
93.Pang, R. Z., Deng, Z. Q., & Chiu, Y. H. (2015). Pareto improvement through a reallocation of carbon emission quotas. Renewable and Sustainable Energy Reviews, 50, 419-430.
94.Peltonen-Sainio, P., Juvonen, J., Korhonen, N., Parkkila, P., Sorvali, J.,& Gregow H.(2021). Climate change, precipitation shifts and early summer drought: An irrigation tipping point for Finnish farmers? Climate Risk Management,33,100334.
95.Pramono, S., Irfan R. M., & Andreo W. A. (2021). Scenario on Indonesian Coal Governance. Technium Social Sciences Journal, 15(1), 1–18.
96.Rahman, M. M. (2020). Environmental degradation: The role of electricity consumption, economic growth and globalisation. Journal of environmental management, 253, 109742.
97.Randazzo, T., Cian, E. D., & Mistry, M. N. (2020). Air conditioning and electricity expenditure: The role of climate in temperate countries. Economic Modelling, 90,273-287.
98.Sharma, R., & Kautish, P. (2020). Examining the nonlinear impact of coal and oil-based electricity production on CO2 emissions in India. The Electricity Journal, 33(6), 106775.
99.Shi, G.-M., Bi, J., & Wang, J.-N.(2010). Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs. Energy policy, 38(10), 6172-6179.
100.Shurui, J., Jingyou, W., Lei, S. H. I., & Zhong, M. (2019). Impact of Energy Consumption and Air Pollution on Economic Growth—An Empirical Study Based on Dynamic Spatial Durbin Model. Energy Procedia, 158, 4011-4016.
101.St-Hilaire, A., Caissie, D., Bergeron, N. E., Ouarda, T. B.M.J.,&  Boyer,C.(2021). Chapter 3 - Climate change and extreme river temperature. Climate Change and Extreme Events,25-37.
102.Tabari, H.(2021). Extreme value analysis dilemma for climate change impact assessment on global flood and extreme precipitation. Journal of Hydrology,593,125932.
103.Teng, X., Liu, F.-P., & Chiu, Y.-H. (2021). The change in energy and carbon emissions efficiency after afforestation in China by applying a modified dynamic SBM model. Energy, 216, 119301.
104.Teng, X., Lu, L. C., & Chiu, Y.-H.(2019). Energy and emission reduction efficiency of China’s industry sector: a non-radial directional distance function analysis. Journal Carbon Management, 10(4), 333-347.
105.Tennakoon, S. D. R., & Mahees , M. T. M. (2021). Environmental Injustice in the water Sector in Sri Lanka. Technium Social Sciences Journal, 18(1), 537–549.
106.Tone, K.(2001). A Slacks-based Measure of Efficiency in Data Envelopment Analysis. European Journal of Operational Research, 130(3),498-509.
107.Tone, K., & Tsutsui, M. (2010). Dynamic DEA: A Slacks-based Measure Approach. Omega, 38, 145-156.
108.Toth, F. L., & Rogner, H.-H.(2006). Oil and nuclear power: past, present, and future. Energy Economics, 28, 1–25.
109.Vlontzos, G., Niavis, S., & Manos, B. (2014). A DEA approach for estimating the agricultural energy and environmental efficiency of EU countries. Renewable and Sustainable Energy Reviews, 40, 91-96.
110.Wang, K., Yu, S., & Zhang, W.(2013). China’s regional energy and environmental efficiency: A DEA window analysis based dynamic evaluation. Mathematical and Computer Modelling, 58(5-6), 1117-1127.
111.Wang, N., Chen, J., Yao, S., & Chang, Y.-C. (2018). A meta-frontier DEA approach to efficiency comparison of carbon reduction technologies on project level. Renewable and Sustainable Energy Reviews, 82, 2606–2612.
112.Wang, Q., Hang, Y., Sun, L., & Zhao, Z. (2016).Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach. Technological Forecasting and Social Change, 112,254–261.
113.Wang, Q., Zhao, Z., Zhou, P., & Zhou, D. (2013). Energy efficiency and production technology heterogeneity in China: a meta-frontier DEA approach. Economic Modelling, 35, 283-289.
114.Wang, S., Zhu, J., Huang, G., Baetz, B., Cheng, G., Zeng, X., & Wang, X. (2020). Assessment of climate change impacts on energy capacity planning in Ontario, Canada using high-resolution regional climate model. Journal of Cleaner Production, 274, 123026.
115.Wen, J., Mughal, N., Zhao, J., Shabbir, M. S., Niedbała, G., Jain, V., & Anwar, A. (2021). Does globalization matter for environmental degradation? Nexus among energy consumption, economic growth, and carbon dioxide emission. Energy Policy, 153, 112230.
116.Wirba, A. V., Mas’ud, A. A., Muhammad-Sukki, F., Ahmad, S., Tahar, R. M., Rahim, A. R., Munir, A. B., & Karim, M. E. (2015). Renewable energy potentials in Cameroon: Prospects and challenges. Renewable Energy, 76, 560–565.
117.Wu, C.-F., Wang, C.-M., Chang, T., & Yuan, C.-C. (2019). The nexus of electricity and economic growth in major economies: The United States-India-China triangle. Energy, 188, 116006.
118.Xin-gang, Z., & Zhen, W. (2019). The technical efficiency of China’s wind power list enterprises: An estimation based on DEA method and micro-data. Renewable Energy, 133, 470–479.
119.Yang, L., & Wang, K.-L.(2013). Regional differences of environmental efficiency of China’s energy utilization and environmental regulation cost based on provincial panel data and DEA method. Mathematical and Computer Modelling, 58(5-6), 1074-1083.
120.Yanqing, X., & Mingsheng, X. (2012). A 3E Model on Energy Consumption, Environment Pollution and Economic Growth---An Empirical Research Based on Panel Data. Energy Procedia, 16, 2011-2018.
121.You, S., Neoh, K. G., Tong, Y. W., Dai, Y., & Wang, C.-H. (2017). Variation of household electricity consumption and potential impact of outdoor PM2. 5 concentration: A comparison between Singapore and Shanghai. Applied energy, 188, 475-484.
122.Zhang, C., Su, B., Zhou, K., & Yang, S. (2019). Analysis of electricity consumption in China (1990–2016) using index decomposition and decoupling approach. Journal of Cleaner Production, 209, 224-235.
123.Zhang, C., Zhou, K., Yang, S., & Shao, Z. (2017). On electricity consumption and economic growth in China. Renewable and Sustainable Energy Reviews, 76, 353-368.
124.Zhang, L., & Gao, J. (2016). Exploring the effects of international tourism on China's economic growth, energy consumption and environmental pollution: Evidence from a regional panel analysis. Renewable and Sustainable Energy Reviews, 53, 225-234.
125.Zhang, L., Zhao, Y., Hein-Griggs, D., Janes, T., Tucker, S.,& Ciborowski, Jan J.H.(2020). Climate change projections of temperature and precipitation for the great lakes basin using the PRECIS regional climate model. Journal of Great Lakes Research,46,2,255-266.
126.Zhang, M., Zhang, K., Hu, W., Zhu, B., Wang, P., & Wei, Y.-M. (2020). Exploring the climatic impacts on residential electricity consumption in Jiangsu, China. Energy Policy, 140, 111398.
127.Zhang, X.-P., Cheng, X.-M., Yuan, J.-H., & Gao, X.-J. (2011). Total-factor energy efficiency in developing countries. Energy Policy, 39(2), 644-650.
128.Zhang, Y.,& Ayyub, B. M.(2021). Chapter 2 - Temperature extremes in a changing climate. Climate Change and Extreme Events,9-23.
129.Zhang, Y.-J., & Peng, H.-R. (2017). Exploring the direct rebound effect of residential electricity consumption: an empirical study in China. Applied energy, 196, 132-141.
130.Zhou, Y., Liu W., Lv, X., Chen, X., & Shen, M.(2019). Investigating interior driving factors and cross-industrial linkages of carbon emission efficiency in China's construction industry: Based on Super-SBM DEA and GVAR model. Journal of Cleaner Production, 241(20), 118322
131.Zhu, D., Zhou, Q., Liu, M.,& Bi, J.(2021). Non-optimum temperature-related mortality burden in China: Addressing the dual influences of climate change and urban heat islands. Science of The Total Environment,782,146760.
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top