Các yếu tố tiềm ẩn ảnh hưởng đến hiệu quả năng lượng: phân tích kinh tế lượng không gian

Springer Science and Business Media LLC - Tập 30 - Trang 13012-13022 - 2022
Xing Wang1,2, Dequn Zhou1,2
1College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China
2Research Center for Soft Energy Science, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Tóm tắt

Việc điều tra nguyên nhân và tác động của hiệu quả năng lượng là lý thuyết và thực tiễn. Tuy nhiên, ít nghiên cứu thực nghiệm đã được thực hiện để xem xét các yếu tố tiềm ẩn cơ bản ảnh hưởng đến hiệu quả năng lượng từ góc độ không gian. Từ đó, chúng tôi kết hợp phân tích bao phủ dữ liệu và phân tích kinh tế lượng không gian để khám phá các yếu tố thúc đẩy hiệu quả năng lượng. Kết quả cho thấy hiệu quả năng lượng của Trung Quốc thể hiện những đặc điểm đáng kể về sự chênh lệch khu vực và sự tập trung không gian; tức là, hiệu quả năng lượng cao đã tạo ra sự tụ hợp lợi ích, trong khi hiệu quả năng lượng thấp tạo ra sự tụ hợp bất lợi. Các kết quả thực nghiệm chỉ ra rằng sự tiến bộ công nghệ, mức độ mở cửa thương mại và đầu tư trực tiếp nước ngoài đã cải thiện hiệu quả năng lượng một cách hiệu quả, trong khi cơ cấu năng lượng và cơ cấu công nghiệp lại có tác động tiêu cực đến hiệu quả năng lượng. Hơn nữa, sự tiến bộ công nghệ, mức độ mở cửa thương mại, cơ cấu năng lượng, đầu tư trực tiếp nước ngoài và cơ cấu công nghiệp tác động khác nhau đến hiệu quả năng lượng, nhưng cơ chế tiềm ẩn của chúng thay đổi đáng kể giữa các khu vực. Do đó, việc sử dụng mô hình kinh tế lượng không gian cho phép sự phụ thuộc không gian trong việc phân tích các yếu tố thúc đẩy hiệu quả năng lượng là cấp bách và cần thiết cho việc ban hành các chính sách năng lượng.

Từ khóa

#hiệu quả năng lượng #phân tích không gian #tiến bộ công nghệ #mở cửa thương mại #đầu tư trực tiếp nước ngoài #cơ cấu ngành

Tài liệu tham khảo

Aguilar-Hernandez GA, Sigüenza-Sanchez CP, Donati F, Rodrigues JF, Tukker A (2018) Assessing circularity interventions: a review of EEIOA-based studies. J Econ Struct 7(1):14 Amowine N, Ma Z, Li M, Zhou Z, Yaw Naminse E, Amowine J (2020) Measuring dynamic energy efficiency in Africa: a slack-based DEA approach. Energy Sci Eng 8(11):3854–3865 Anselin L (2010) Thirty years of spatial econometrics. Regional Science 89(1):3–25 Anselin L, Le Gallo J, Jayet H (2006) Spatial panel econometrics. In: Matyas L, Sevestre P (eds) The econometrics of panel data, fundamentals and recent developments in theory and practice, 3rd edn. Kluwer, Dordrecht, pp 901–969 Charnes A, Coope W, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444 Cheng ZH, Liu J, Li LS, Gu XB (2020a) Research on meta-frontier total-factor energy efficiency and its spatial convergence in Chinese provinces. Energy Eco 86:104702 Cheng ZH, Liu J, Li LS, Gu XB (2020b) Research on meta-frontier total-factor energy efficiency and its spatial convergence in Chinese provinces. Energy Pol 86:104702 Chong CH, Ma L, Li Z, Ni W, Song S (2015) Logarithmic mean divisia index (LMDI) decomposition of coal consumption in China based on the energy allocation diagram of coal flows. Energy 85:366–378 Chu J, Wu J, Zhu Q, An Q, Xiong B (2016) Analysis of China’s regional ecoefficiency: a DEA two-stage network approach with equitable efficiency decomposition. Computational Econ 1:1–23 Davis J-M, Akese G, Garb Y (2019) Beyond the pollution haven hypothesis: where and why do e-waste hubs emerge and what does this mean for policies and interventions? Geoforum 98:36–45 Doytch N, Narayan S (2016) Does FDI influence renewable energy consumption? An analysis of sectoral FDI impact on renewable and nonrenewable industrial energy consumption. Energy Econ 54:291–301 Fan R, Luo M, Zhang P (2016) A study on evolution of energy intensity in China with heterogeneity and rebound effect. Energy 99:159–169 Fang CY, Hu JL, Lou TK (2013) Environment-adjusted total-factor energy efficiency of Taiwan’s service sectors. Energy Pol 63:1160–1168 Feng C, Wang M, Liu G, Huang J (2017) Green development performance and its influencing factors: a global perspective. J Clean Prod 144:323–333 Friedl B, Gettzer M (2003) Determinants of CO2 emissions in a small open economy. Ecol Econ 45(1):133–148 Ghosh NK, Blackhurst MF (2014) Energy savings and the rebound effects with multiple energy services and efficiency correlation. Ecol Econ 105(105):55–66 Haider S, Mishra PP (2021) Does innovative capability enhance the energy efficiency of Indian iron and steel firms? A Bayesian stochastic frontier analysis. Energy Pol 95:105128 He Y, Rao J-W, Fu F-F (2020) Dynamic analysis of factors affecting China’s industrial energy efficiency based on co-integration and error correction models. Mathematics in Practice and Knowledge 3:37–47 Hu LB, Hu JL (2012) Ecological total-factor energy efficiency of regions in China. Energy Pol 46:216–224 Javid M, Khan M (2020) Energy efficiency and underlying carbon emission trends. Environ Sci Pollut Res 27(3):3224–3236 LeSage JP, Pace RK (2009) Introduction to spatial econometrics. CRC Press/Taylor& Francis Group, London Li DQ, Wang DY (2016) Decomposition analysis of energy consumption for a freeway during its operation period: a case study for Guangdong, China. Energy 97:296–305 Liu J, Cheng Z, Zhang H (2017) Does industrial agglomeration promote the increase of energy efficiency in China. J Clean Prod 164:30–37 Liu Z, Zhang H, Zhang Y-J, Qin C-X (2020) How does income inequality affect energy efficiency? Empirical evidence from 33 Belt and Road initiative countries. J Clean Prod 269:122421 Lorenzo-Toja Y, Vazquez-Rowe I, Marín-Navarro D, Crujeiras RM, Moreira MT, Feijoo G (2018) Dynamic environmental efficiency assessment for wastewater treatment plants. Int J Life Cycle Assess 23(2):357–367 Lv K, Bian Y, Yu A (2018) Environmental efficiency evaluation of industrial sector in China by incorporating learning effects. J Clean Prod 172:2464–2474 Moshiri S, Aliyev K (2017) Rebound effect of efficiency improvement in passenger cars on gasoline consumption in Canada. Ecol Econ 131:330–341 Pan H, Zhang H, Zhang X (2013) China’s provincial industrial energy efficiency and its determinants. Math Comput Model 58(5–6):1032–1039 Pinkse J, Slade M (2010) The future of spatial econometrics. J Reg Sci 50(1):103–117 Qu CY, Shao J, Shi ZK (2020) Does financial agglomeration promote the increase of energy efficiency in China? Energy Pol 146:111810 Ruzzenenti F, Basosi R (2017) Modelling the rebound effect with network theory: an insight into the European freight transport sector. Energy 118:272–283 Salman H, Prajna P (2021) Does innovative capability enhance the energy efficiency of Indian iron and steel firms? A Bayesian stochastic frontier analysis. Energy Econ 95:105128 Song M, Guan Y (2014) The environmental efficiency of wanjiang demonstration area: a Bayesian estimation approach. Ecol Indic 36:59–67 Song M, Wang S, Sun J (2018) Environmental regulations, staff quality, green technology, R&D. Efficiency, and profit in manufacturing. Technol Forecast Soc Change 133:1–14 Takayabu H (2020) CO2 mitigation potentials in manufacturing sectors of 26 countries. Energy Econ 86:104634 Tao X, Wang P, Zhu B (2016) Provincial green economic efficiency of China: a non-separable input-output SBM approach. Appl Energy 171:58–66 Timma L, Zoss T, Blumberga D (2015) Life after the financial crisis. Energy intensity and energy use decomposition on sectorial level in Latvia. Appl. Energy 162:1586–1592 Tone K (2001) A slacks-based measure of efficiency in data envelopment analysis. Eur J Oper Res 130(3):498–509 Vivanco DF, Mcdowall W et al (2016) The foundations of the environmental rebound effects and its contribution towards a general framework. Ecol Econ 125:60–69 Wang X, Zhou DQ (2021) Spatial agglomeration and driving factors of environmental pollution: a spatial analysis. J Clean Prod 279:123839 Wang X, Zhou DQ, Telli Ş (2022a) The impact of semi-urbanization on carbon emissions: a spatial econometric perspective. Environ Sci Pollut Res 29:54718-54732 Wang JD, Dong XC, Dong KY (2022b) How does ICT agglomeration affect carbon emissions? The case of Yangtze River Delta urban agglomeration in China. Energy Econ 111:106107 Wang JY, Wang K, Shi XP, Wei YM (2019) Spatial heterogeneity and driving forces of environmental productivity growth in China: would it help to switch pollutant discharge fees to environmental taxes? J Clean Prod 223:36–44 Wang R, Tan JL (2020) Exploring the coupling and forecasting of financial development, technological innovation, and economic growth. Technol Forecast Soc Change 163:120466 Wei Z, Han B, Pan X et al (2020) Effects of diversified openness channels on the total-factor energy efficiency in China’s manufacturing sub-sectors: evidence from trade and FDI spillovers. Energy Econ 90:104836 World Bank (2021) World development indicators 2021 Wu H, Hao Y, Weng JH (2019) How does energy consumption affect China’s urbanization? New evidence from dynamic threshold panel models. Energy Policy 127:24–38 Wu HT, Hao Y, Ren SY (2020) How do environmental regulation and environmental decentralization affect green total factor energy efficiency: evidence from China. Energy Eco 91:104880 Xu X, Huang SP, An HZ (2021) Identification and causal analysis of the influence channels of financial development on CO2 emissions. Energy Pol 153:112277 Zhang M, Song Y, Li P, Li H (2016) Study on affecting factors of residential energy consumption in urban and rural Jiangsu. Renew Sustain Energy Rev 53:330–337 Zhao J, Jiang QZ, Dong XC, Dong KY, Jiang HD (2022) How does industrial structure adjustment reduce CO2 emissions? Spatial and mediation effects analysis for China. Energy Econ 105:105704 Zhao LX, Feng KL, Zhao R (2020) The relationship between heterogeneous environmental regulations, system quality and green total factor productivity. Sci Technol Manag Res 40(22):214–222 Zheng S, Lam C, Hsu S, Ren J (2018) Evaluating efficiency of energy conservation measures in energy service companies in China. Energy Pol 122:580–591 Zhu L, Hao Y, Lu ZN, Wu H, Ran Q (2019) Do economic activities cause air pollution? Evidence from China’s major cities. Sustain Cities Soc 49:101593