RESEARCH ON CARBON EMISSIONS PEAKING AND LOW-CARBON DEVELOPMENT OF CITIES: A CASE OF SHANGHAI
-
摘要: 在2015年巴黎气候变化大会上,中国政府提出"2030年前后碳排放达到峰值并加快实现"等一系列新阶段目标。城市是能源资源消耗和碳排放的集聚区域,推动城市低碳发展成为各国面临的共同挑战。采用STIRPAT模型,探究上海市过去20年发展总体形势,分析碳排放影响因素,判断上海在2025年是否可以达峰。结果表明:无论基准情景还是超低碳情景,上海市在2025年之前达峰的目标均可以实现。在对上海市碳排放各影响因素中,城市化率对其影响最大,其次是人均GDP水平。Abstract: On the 2015 Paris Climate Change Conference, Chinese government put forward a series of new goals such as "peak carbon emissions around 2030 and accelerate their realization". Cities are main sources of energy consumption and carbon emissions, thus promoting low-carbon development of cities becomes a common challenge facing all countries. This study used the STIRPAT model to explore the development situation of Shanghai in the past 20 years, and analyzed the impact factors of its carbon emissions to identity whether Shanghai can peak carbon emissions in 2025. The results showed that shanghai could peak its carbon emissions by 2025 under either the baseline scenario or the ultra-low carbon scenario. It was also found that urbanization rate was the dominant impact factors for Shanghai’s carbon emissions, followed by the level of per capita GDP.
-
Key words:
- climate change /
- peak carbon emissions /
- low-carbon cities /
- STIRPAT model
-
UNFCCC. The Paris Agreement[R]. New York, 2015. 李海鹰. 浅谈巴黎气候大会后中国碳减排问题[J]. 现代商业, 2017(11):163-164. 冯辰, 郭秀锐, 路路. 北京市能源需求及碳排放情景分析[D].北京:北京工业大学, 2012. 上海市人民政府. 上海市城市总体规划(2017-2035年)[R]. 上海, 2017. AL-MULALI U, FEREIDOUNI H G, LEE J Y M. Electricity consumption from renewable and non-renewable sources and economic growth:evidence from Latin American countries[J]. Renewable and Sustainable Energy Reviews, 2014, 30:290-298. KASMAN A, DUMAN Y S. CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries:a panel data analysis[J]. Economic Modelling, 2015, 44:97-103. ZOUNDI Z. CO2 emissions, renewable energy and the Environmental Kuznets Curve, a panel cointegration approach[J]. Renewable & Sustainable Energy Reviews, 2016. 许广月, 宋德勇. 中国碳排放环境库兹涅茨曲线的实证研究:基于省域面板数据[J]. 中国工业经济, 2010(5):37-47. 刘华军, 闫庆悦, 孙曰瑶. 中国二氧化碳排放的环境库兹涅茨曲线:基于时间序列与面板数据的经验估计[J]. 中国科技论坛, 2011(4):108-113. 杜强, 冯新宇, 孙强. 市域建筑业碳排放与经济发展关系及影响因素研究:以西安市为例[J]. 环境工程, 2017, 35(2):174-179. 李国志. 基于变参数模型的中国碳排放与经济增长EKC拐点研究[J]. 环境工程, 2018, 36(2):142-146. FRIEDL B, GETZNER M. Determinants of CO2 emissions in a small open economy[J]. Ecological Economics, 2003, 45(1):133-148. FODHA M, ZAGHDOUD O. Economic growth and pollutant emissions in Tunisia:an empirical analysis of the environmental Kuznets curve[J]. Energy Policy, 2010, 38(2):1150-1156. 郑长德, 刘帅. 基于空间计量经济学的碳排放与经济增长分析[J]. 中国人口·资源与环境, 2011, 21(5):80-86. CHAI Q M, XU H Q. Modeling an emissions peak in China around 2030:synergies or trade-offs between economy, energy and climate security[J]. Advances in Climate Change Research, 2014, 5(4):169-180. NIU S W, LIU Y Y, DING Y X, et al. China's energy systems transformation and emissions peak[J]. Renewable and Sustainable Energy Reviews, 2016, 58:782-795. ZHANG X L, KARPLUS V J, QI T Y, et al. Carbon emissions in China:How far can new efforts bend the curve?[J]. Energy Economics, 2016, 54:388-395. YANG X, TENG F. Air quality benefit of China's mitigation target to peak its emission by 2030[J]. Climate Policy, 2017, 18(1):99-110. MI Z F, WEI Y M, WANG B, et al. Socioeconomic impact assessment of China's CO2 emissions peak prior to 2030[J]. Journal of Cleaner Production, 2017, 142(4):2227-2236. GALLAGHER K S, ZHANG F, ORVIS R, et al. Assessing the Policy gaps for achieving China's climate targets in the Paris Agreement[J]. Nature Communications, 2019, 10(1):1256. ZHOU N, PRICE L, YANDE D, et al. A roadmap for China to peak carbon dioxide emissions and achieve a 20% share of non-fossil fuels in primary energy by 2030[J]. Applied Energy, 2019, 239:793-819. WANG H K, LU X, DENG Y, et al. China's CO2 peak before 2030 implied from characteristics and growth of cities[J]. Nature Sustainability, 2019, 2(8):748-754. 曹斌, 林剑艺, 崔胜辉, 等. 基于LEAP的厦门市节能与温室气体减排潜力情景分析[J]. 生态学报, 2010, 30(12):3358-3367. 龙妍, 丰文先, 王兴辉. 基于LEAP模型的湖北省能源消耗及碳排放分析[J]. 电力科学与工程, 2016, 32(5):1-6,19. 邱硕, 王雪强, 毕胜山, 等. LEAP模型下的陕西省节能与温室气体减排潜力分析[J]. 西安交通大学学报, 2016, 50(11):28-35. 冯宗宪, 王安静. 陕西省碳排放因素分解与碳峰值预测研究[J]. 西南民族大学学报(人文社科版), 2016, 37(8):112-119. 邓小乐, 孙慧. 基于STIRPAT模型的西北五省区碳排放峰值预测研究[J]. 生态经济, 2016, 32(9):36-41. 李强, 左静娴. 基于STIRPAT模型的长江经济带碳排放峰值预测研究[J]. 东北农业大学学报(社会科学版), 2017, 15(5):53-58. 毕莹, 杨方白. 辽宁省碳排放影响因素分析及达峰情景预测[J]. 东北财经大学学报, 2017(4):91-97. 陈丽君, 吴红梅, 范玲, 等. 浙江省碳排放峰值判断及其对策研究[J]. 中国能源, 2017, 39(4):43-47. 上海市统计局. 2019年上海市国民经济和社会发展统计公报[EB/OL]. http://tjj.sh.gov.cn/tjgb/20200329/05f0f4abb2d448a69e4517f6a6448819.html. 2020-3-9. Bank W. Global Economic Prospects, June 2020[EB/OL]. https://openknowledge.worldbank.org/handle/10986/33748. 2020-6. OECD. Coronavirus:The world economy at risk[EB/OL]. http://www.oecd.org/economic-outlook/march-2020/. 2020-3-2. 徐丽娜. 城镇化进程中山西省碳排放量影响因素分析及预测研究[D]. 天津:天津大学, 2014. 王世进. 我国城镇化进程中碳排放影响因素的实证研究[J]. 环境工程, 2017, 35(6):146-150. 何永贵, 于江浩. 基于STIRPAT模型的我国碳排放和产业结构优化研究[J]. 环境工程, 2018, 36(7):174-178,184. 上海市人民政府. 上海市能源发展"十三五"规划[EB/OL]. http://www.shanghai.gov.cn/nw2/nw2314/nw2319/nw12344/u26aw51932.html. 2017-3-15. 李欢, 杨珊, 陈建宏, 等. 湖南省能源消费碳排放驱动因素及趋势预测实证分析[J]. 环境工程, 2018, 36(2):152-157.
点击查看大图
计量
- 文章访问数: 479
- HTML全文浏览量: 29
- PDF下载量: 39
- 被引次数: 0