广东工业大学学报 ›› 2021, Vol. 38 ›› Issue (05): 90-96.doi: 10.12052/gdutxb.200145

• • 上一篇    下一篇

农村用电碳排放变化驱动因素研究

卢晓晴1, 方媛1, 梁泽奇2   

  1. 1. 广东工业大学 土木与交通工程学院,广东 广州 510006;
    2. 肇庆市供电局,广东 肇庆 526300
  • 收稿日期:2020-10-30 出版日期:2021-09-10 发布日期:2021-07-13
  • 通信作者: 方媛(1978–),女,讲师,博士,硕士生导师,主要研究方向为可持续建设,E-mail:carolynfang@gdut.edu.cn E-mail:carolynfang@gdut.edu.cn
  • 作者简介:卢晓晴(1997–),女,硕士研究生,主要研究方向为可持续建设
  • 基金资助:
    国家自然科学基金资助项目(51608132)

A Driving Factors Analysis of the Carbon Emission Change of Rural Electricity Consumption

Lu Xiao-qing1, Fang Yuan1, Liang Ze-qi2   

  1. 1. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China;
    2. Zhaoqing Power Supply Bureau, Zhaoqing 526300, China
  • Received:2020-10-30 Online:2021-09-10 Published:2021-07-13

摘要: 随着农村经济的不断发展, 农村能源消费正逐步由生物质能源向电力转变。因此, 从农村视角识别影响农村用电碳排放的潜在驱动因素, 是我国推进农村低碳发展、实现碳减排目标的重要前提。本文采用基于人口、富裕度和技术的随机影响回归模型(Stochastic Impacts by Regression on Population, Affluence and Technology, STIRPAT), 结合岭回归, 分析2005~2018年广东省农村用电碳排放的影响因素。结果表明, 城市化水平、农业经济发展水平、有效灌溉面积、农业机械化水平、农业固定资产投资、收入水平以及住房条件等因素都与广东省农村用电产生的碳排放呈正相关。其中, 农村居民人均可支配收入是影响广东省农村用电碳排放的最关键因素。此外, 农业经济发展与广东省农村用电碳排放也呈弱脱钩关系。最后, 针对如何减少广东省农村用电碳排放提出政策建议。

关键词: 碳排放, 农村用电, 脱钩分析

Abstract: With the continuous development of rural economy, rural energy consumption is gradually changing from biomass energy to electricity. Therefore, identifying the potential driving factors of rural electricity carbon emissions from the perspective of rural areas is an important prerequisite for promoting rural low-carbon development and achieving carbon emission reduction goals. In this study, Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and ridge regression are used to analyze the influencing factors of carbon emissions from rural electricity consumption in Guangdong Province from 2005 to 2018. The results show that urbanization level, agricultural economic development level, effective irrigation area, agricultural mechanization level, agricultural fixed assets investment, income level and housing conditions are positively correlated with carbon emissions from rural power consumption. Among them, the per capita disposable income of rural residents is the most important factor affecting the carbon emission of rural electricity. In addition, there is a weak decoupling relationship between agricultural economic development and rural electricity carbon emissions. Finally, policy suggestions are put forward on how to reduce rural electricity carbon emissions in Guangdong Province.

Key words: carbon emissions, rural electricity consumption, decoupling analysis

中图分类号: 

  • F282
[1] PAN W, TU H T, HU C, et al. Driving forces of China's multisector CO2 emissions: a Log-Mean Divisia Index decomposition[J]. Environmental Science and Pollution Research, 2020, 27: 23550-23564.
[2] WU Y, CHAU K W, LU W S, et al. Decoupling relationship between economic output and carbon emission in the Chinese construction industry[J]. Environmental Impact Assessment Review, 2018, 71: 60-69.
[3] MA X J, WANG C X, DONG B Y, et al. Carbon emissions from energy consumption in China: Its measurement and driving factors[J]. The science of the Total Environment, 2018, 648: 1411-1420.
[4] WANG P, WU W S, ZHU B Z, et al. Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China[J]. Applied Energy, 2013, 106: 65-71.
[5] WANG C J, WANG F, ZHANG X L, et al. Examining the driving factors of energy related carbon emissions using the extended STIRPAT model based on IPAT identity in Xinjiang[J]. Renewable and Sustainable Energy Reviews, 2017, 67: 51-61.
[6] XU B, LIN B Q. Factors affecting CO2 emissions in China's agriculture sector: Evidence from geographically weighted regression model [J]. Energy Policy, 2017, 104: 404-414.
[7] CHEN Y, LI M, SU K, et al. Spatial-Temporal Characteristics of the Driving Factors of Agricultural Carbon Emissions: Empirical Evidence from Fujian, China[J]. Energies, 2019, 12.
[8] LIU Y, TANG H, MUHAMMAD A, et al. Emission mechanism and reduction countermeasures of agricultural greenhouse gases —— a review [J]. Greenhouse Gases-Science and Technology, 2019, 9(2): 160-174.
[9] XIONG C H, YANG D G, XIA F Q, et al. Changes in agricultural carbon emissions and factors that influence agricultural carbon emissions based on different stages in Xinjiang, China [J]. Scientific Reports, 2016, 6(1).
[10] HAN H B, ZHONG Z Q, GUO Y, et al. Coupling and decoupling effects of agricultural carbon emissions in china and their driving factors [J]. Environmental Science and Pollution Research, 2018, 25(9): 1-14.
[11] LI N, WEI C D, ZHANG H, et al. Drivers of the national and regional crop production-derived greenhouse gas emissions in China[J]. Journal of Cleaner Production, 2020, 257.
[12] ZHEN W, QIN Q D, KUANG Y Q, et al. Investigating low-carbon crop production in Guangdong province, China (1993–2013): a decoupling and decomposition analysis[J]. Journal of Cleaner Production, 2017, 146: 63-70.
[13] XIONG C H, CHEN S, XU L T. Driving factors analysis of agricultural carbon emissions based on extended STIRPAT model of Jiangsu Province, China [J]. Growth and Change, 2020, 51(3): 1401-1416.
[14] YANG Y, JIA J S, CHEN C D. Residential Energy-Related CO2 Emissions in China’s Less Developed Regions: A Case Study of Jiangxi [J]. Sustainability, 2020, 12(5).
[15] WANG Z H, YANG L. Indirect carbon emissions in household consumption: evidence from the urban and rural area in China[J]. Journal of Cleaner Production, 2014, 78: 94-103.
[16] CHEN Q, YANG H R, WANG W G, et al. Beyond the City: Effects of Urbanization on Rural Residential Energy Intensity and CO2 Emissions [J]. Sustainability, 2019, 11(8).
[17] ZHA D L, ZHOU D Q, ZHOU P. Driving forces of residential CO2 emissions in urban and rural China: An index decomposition analysis [J]. Energy Policy, 2010, 38(7): 3377-3383.
[18] FAN J B, RAN A, LI X M. A study on the factors affecting china's direct household carbon emission and comparison of regional differences [J]. Sustainability, 2019, 11(18).
[19] WANG W X, ZHAO D Q, KUANG Y Q. Decomposition analysis on influence factors of direct household energy-related carbon emission in Guangdong province —— Based on extended Kaya identity [J]. Sustainable Energy, 2016, 35(1): 298-307.
[20] DONG Y M, ZHAO T. Difference analysis of the relationship between household per capita income, per capita expenditure and per capita CO2 emissions in China: 1997—2014 [J]. Atmospheric Pollution Research, 2017, 8(2): 310-319.
[21] QIU H G, YAN J B, LEI Z, et al. Rising wages and energy consumption transition in rural China[J]. Energy Policy, 2018, 119: 545-553.
[22] SALAHUDDIN M, GOW J, OZTURK I. Is the long-run relationship between economic growth, electricity consumption, carbon dioxide emissions and financial development in Gulf Cooperation Council Countries robust?[J]. Renewable & Sustainable Energy Reviews, 2015, 51: 317-326.
[23] ANG B W. Decomposition analysis for policy making in energy: which is the preferred method? [J]. Energy Policy, 2004, 32(9): 1131-1139.
[24] YORK R, ROSA E A, DIETZ T. STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts [J]. Ecological Economics, 2003, 46(3): 351-365.
[25] ANG B W, LIU F L, CHUNG H S. A generalized Fisher index approach to energy decomposition analysis [J]. Energy Economics, 2004, 26(5): 757-763.
[26] LI W, SHEN Y B, ZHANG H X. A Factor Decomposition on China's Carbon Emission from 1997 to 2012 Based on IPAT-LMDI Model[J]. Mathematical Problems in Engineering, 2015, 2015: 1-14.
[27] TAPIO P. Towards a theory of decoupling: degrees of decoupling in the EU and the case of road traffic in Finland between 1970 and 2001 [J]. Transport Policy, 2005, 12(2): 137-151.
[28] LI X, HE X, LUO X, et al. Exploring the characteristics and drivers of indirect energy consumption of urban and rural households from a sectoral perspective [J]. Greenhouse Gases-Science and Technology, 2020, 10(5): 907-924.
[29] MIAO L, GU H, ZHANG X, et al. Factors causing regional differences in China's residential CO2 emissions — Evidence from provincial data[J]. Journal of Cleaner Production, 2019, 224: 852-863.
[1] 彭美春, 阳晨, 李君平, 叶伟斌, 黄文伟. 基于BP神经网络的车辆碳排放测算研究[J]. 广东工业大学学报, 2023, 40(01): 107-112.
[2] 彭美春, 廖清睿, 曾隆隆, 王嘉浩. 道路营运新能源汽车减碳测算[J]. 广东工业大学学报, 2020, 37(02): 39-44.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!