海岛城市碳排放测度及其影响因素分析——以浙江省舟山市为例

doi:10.11821/dlyj201805013

地理研究 ›› 2018, Vol. 37 ›› Issue (5): 1023-1033.doi: 10.11821/dlyj201805013

海岛城市碳排放测度及其影响因素分析——以浙江省舟山市为例

孙艳伟¹(), 李加林^1,², 李伟芳¹, 马仁锋¹

1. 宁波大学地理与空间信息技术系,宁波 315211
2. 浙江省海洋文化与经济研究中心,宁波 315211

收稿日期:2017-12-26 修回日期:2018-02-27 出版日期:2018-05-20 发布日期:2018-05-20
作者简介:
作者简介：孙艳伟（1983- ）,男,内蒙古赤峰人,博士,讲师,研究方向为城市地理与环境经济管理。E-mail: sunyanwei@nbu.edu.cn
基金资助:
浙江省软科学项目（2016C35G2070007）

Estimating the carbon emissions and influencing factors of island city: A case study in Zhoushan Islands, Zhejiang province

Yanwei SUN¹(), Jialin LI^1,², Weifang LI¹, Renfeng MA¹

1. Department of Geography & Spatial Information Technology, Ningbo University, Ningbo 315211, Zhejiang, China;
2. Marine Culture and Economic Research Center, Zhejiang Province, Ningbo 315211, Zhejiang, China

Received:2017-12-26 Revised:2018-02-27 Online:2018-05-20 Published:2018-05-20
About author:
Author: Shi Zhenqin (1988-), PhD, specialized in regional development and land space management in mountain areas. E-mail: kevinszq@163.com

^*Corresponding author: Deng Wei (1957-), Professor, specialized in mountain environment and regional development.

E-mail: dengwei@imde.ac.cn

摘要/Abstract

摘要：

海岛城市具有独特的社会经济发展轨迹以及碳排放特征。定量剖析海岛城市碳排放的演变趋势及其关键驱动因子,对于指导“低碳海岛城市”和“生态海岛城市”建设意义重大。以中国典型海岛城市舟山市为案例区,采用IPCC参考方法,测算2001-2015年间舟山市各部门的碳排放量,并运用STIRPAT扩展模型,定量分析能源强度、人均GDP以及城市化率等关键驱动因子对海岛城市碳排放的影响;在此基础上,结合情景分析技术对舟山市未来的碳排放量进行情景预测。结果表明：① 2001-2015年间,舟山市碳排放量增加迅速,年均增长率达到32%,碳排放强度遵从先降后增的“U型”变化规律,年均值略高于厦门市。② 岭回归分析表明城市化率对碳排放量的增加影响最大,其次为能源强度,人均GDP对碳排放的影响最弱。③ 舟山市经济增长与碳排放之间存在Kuznets曲线假说,且理论拐点将出现在人均GDP为10.46万元附近（2000年可比价）。④ 舟山市在2020年和2030年的碳排放总量将分别达到2142万t和4333万t。总体而言,产业结构低碳化和能源效率提升是缓解舟山市未来碳减排压力的有力抓手。

关键词: 海岛城市, 碳排放, STIRPAT模型, 影响因素, 舟山市

Abstract:

Island cities have unique economic development paths and characteristics of carbon emissions. Quantitatively examining the evolution trends and influencing factors of carbon emissions in island cities will play an important role in guiding the constructions of "low-carbon city" and "eco-city". In this study, we chose Zhoushan Islands as a case study and used the IPCC guidelines methods to estimate its multi-sectoral carbon emissions during 2001-2015. Meanwhile, the STIRPAT model was applied to analyze the effects of energy intensity, GDP per capita and urbanization rate on carbon emissions. Based on the carbon emission effects, the future carbon emissions of Zhoushan Islands were then predicted by setting various socio-economic scenarios. The results indicated that the carbon emissions in Zhoushan have a dramatical increase, with an annual growth rate of 32% since 2001, while carbon emissions intensity followed the U-shaped changing trend, and its annual mean value was higher than that of Xiamen city. In addition, the disaggregated analysis revealed urbanization rate was the most robust driving factor of increasing carbon emissions, and energy intensity was the secondary, and GDP per capita was the weakest factor. We also found evidences in support of inverted U-shaped curve relationship between economic growth and carbon emissions, and the theory inflection point of CO₂ emissions was found at 10.46×10⁴ yuan of GDP per capita (calculated at 2000 comparable prices). According to our prediction, the total amount of carbon emissions in Zhoushan Islands by 2020 and 2030 may reach 2142×10⁴ tCO₂e, and 4333×10⁴ tCO₂e, respectively. Overall, we suggested that low-carbonization of industrial structure and the improvement of energy efficiency would be the effective measures for future carbon emissions reduction of Zhoushan Islands.

Key words: island city, carbon emissions, STIRPAT model, influencing factors, Zhoushan Islands

孙艳伟, 李加林, 李伟芳, 马仁锋. 海岛城市碳排放测度及其影响因素分析——以浙江省舟山市为例[J]. 地理研究, 2018, 37(5): 1023-1033.

Yanwei SUN, Jialin LI, Weifang LI, Renfeng MA. Estimating the carbon emissions and influencing factors of island city: A case study in Zhoushan Islands, Zhejiang province[J]. GEOGRAPHICAL RESEARCH, 2018, 37(5): 1023-1033.

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燃料类型	原煤	洗精煤	其他洗煤	型煤	焦煤	焦炉煤气	天然气	液化天然气	原油	汽油	煤油	柴油	燃料油	液化石油气	其他石油制品	热力	电力
折标煤系数(kg/kg)	0.71	0.90	0.28	0.97	0.97	0.57	1.33	1.71	1.43	1.47	1.47	1.46	1.43	1.71	1.43	0.03	0.12
排放因子(t/t)	1.98	2.49	0.79	1.72	3.02	7.42	21.84	2.84	3.1	3.18	3.15	3.18	3.13	2.98	2.95	0.26	9.26

情景	“十三五”	“十四五”	“十五五”
基准情景	10.3	9.3	8.3
低值情景	9.3	8.3	7.3
高值情景	12.2	11.2	10.2

情景	“十三五”期末	“十四五”期末	“十五五”期末
基准情景	0.52	0.44	0.37
低值情景	0.55	0.49	0.44
高值情景	0.49	0.39	0.31

情景	经济增长	能源强度	城市化
情景1	基准方案	基准方案	基准方案
情景2	基准方案	低值方案	基准方案
情景3	基准方案	高值方案	基准方案
情景4	低值方案	基准方案	基准方案
情景5	低值方案	低值方案	基准方案
情景6	低值方案	高值方案	基准方案
情景7	高值方案	基准方案	基准方案
情景8	高值方案	低值方案	基准方案
情景9	高值方案	高值方案	基准方案

变量	系数	标准误差	标准系数	t统计值	Sig.	容差	VIF
常数	-0.496	3.185		-0.156	0.879
lnA	1.901	0.275	2.103	6.906	0.000	0.001	855.662
(lnA)²	0.142	0.031	0.921	4.496	0.001	0.003	387.282
lnE_d	0.688	0.032	0.307	21.205	0.000	0.515	1.940
lnU	-1.386	1.371	-0.137	-1.011	0.336	0.006	168.935

海岛城市碳排放测度及其影响因素分析——以浙江省舟山市为例

Estimating the carbon emissions and influencing factors of island city: A case study in Zhoushan Islands, Zhejiang province

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参考文献 24

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变量	系数	标准误差	标准系数	t统计值	Sig.
常数	-9.582	0.868		8.457	0.000
lnA	0.253	0.020	0.278	12.800	0.000
(lnA)²	-0.012	0.003	-0.259	-11.510	0.000
lnE_d	0.389	0.107	0.160	3.344	0.007
lnU	2.961	0.251	0.197	12.006	0.000

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