
Evaluation of carbon emission efficiency of resource-based cities and its policy enlightenment
ZHANG Ming-dou, XI Sheng-jie
JOURNAL OF NATURAL RESOURCES ›› 2023, Vol. 38 ›› Issue (1) : 220-237.
Evaluation of carbon emission efficiency of resource-based cities and its policy enlightenment
Based on the dual pressures of economic transformation and energy conservation and emission reduction faced by resource-based cities, this paper empirically measures the carbon emission efficiency of 114 resource-based cities in China from 2004 to 2019 by using a three-stage super-efficiency SBM model, and discusses the efficiency differences, spatio-temporal evolution and sources of differences by using the methods of Dagum Gini coefficient, kernel density estimation and QAP regression analysis. The results show that: (1) After the environmental factors and random interference is removed, the average carbon emission efficiency of resource-based cities decreases to 0.230, but the overall trend is on the rise. (2) The areas with high carbon emission efficiency are concentrated in regenerating and mature cities, and the distribution is relatively scattered. The low value areas are mainly growing and declining cities, and they are clustered in northeast, central and other regions. (3) The carbon emission efficiency of resource-based cities varies greatly, among which the contribution rate of inter-group difference is the highest, but it shows a downward trend; The differences within the groups of regenerating and mature cities are relatively stable, while the differences within the groups of growing and declining cities are gradually expanding. (4) The differences of population density, per capita disposable income and the number of large-scale enterprises will significantly expand the differences of carbon emission efficiency of resource-based cities, and the differences in population density, population structure and disposable income of residents have a significant heterogeneous impact on the differences in carbon emission efficiency of different types of resource-based cities. Considering the characteristics of carbon emission efficiency of resource-based cities, this paper puts forward the following policy implications: Firstly, according to the types of resource-based cities, we should adopt emission reduction schemes that suit local conditions. Secondly, we should increase investment in low-carbon innovation and promote green transformation of industry. Resource-based cities can increase R&D investment by imitating innovation, and make low-carbon innovation more directional and targeted. At the same time, resource-based cities should encourage enterprises to carry out green technological transformation and upgrading. Thirdly, we will promote green consumption mode and strengthen emission reduction of residents. The government should enhance residents' cognitive level of consumption emission reduction ability and encourage consumers to make low-carbon consumption. Moreover, the government should explore green financial innovation, establish individual carbon credit and carbon account system, and encourage residents' participation in green financial market.
resource-based cities / carbon emission efficiency / source of difference / three stage super-efficiency SBM model {{custom_keyword}} /
Table 1 Second stage SFA regression results表1 第二阶段SFA回归结果 |
变量 | 资本存量松弛量 | 就业人数松弛量 | 能源投入松弛量 |
---|---|---|---|
常数项 | -525.046*** (-8.190) | -3.292 (-1.214) | 275.703*** (15.631) |
产业结构 | 678.162*** (5.628) | 12.788*** (2.767) | -11.597 (-0.496) |
科技投入水平 | 19341.987*** (16.094) | 152.493*** (3.090) | 994.766*** (664.017) |
环境规制水平 | -34.888** (-2.553) | 0.237 (0.493) | 14.955*** (9.237) |
经济开放水平 | 18.909*** (3.707) | 0.072 (0.462) | -0.730 (-1.102) |
| 2787277.900*** (238411.520) | 1161.639*** (8.339) | 21086.387*** (18927.674) |
γ | 0.920*** (362.727) | 0.766*** (26.415) | 0.777*** (106.443) |
对数似然函数值 | -14385.034 | -7817.007 | -10643.103 |
LR单边检验 | 2826.814*** | 560.952*** | 866.380*** |
注:*、**、***分别表示10%、5%、1%显著性水平,括号内为T统计量。 |
Table 2 Inter-regional Gini coefficient of carbon emission efficiency of resource-based cities表2 资源型城市碳排放效率的组间基尼系数 |
年份 | 再生型— 成熟型 | 再生型— 成长型 | 再生型— 衰退型 | 成熟型— 成长型 | 成熟型— 衰退型 | 成长型— 衰退型 |
---|---|---|---|---|---|---|
2004 | 0.563 | 0.674 | 0.683 | 0.465 | 0.477 | 0.385 |
2005 | 0.567 | 0.672 | 0.673 | 0.446 | 0.464 | 0.372 |
2006 | 0.559 | 0.663 | 0.662 | 0.440 | 0.457 | 0.370 |
2007 | 0.568 | 0.665 | 0.667 | 0.449 | 0.465 | 0.374 |
2008 | 0.576 | 0.664 | 0.673 | 0.438 | 0.450 | 0.365 |
2009 | 0.562 | 0.648 | 0.655 | 0.440 | 0.444 | 0.363 |
2010 | 0.539 | 0.632 | 0.618 | 0.424 | 0.427 | 0.366 |
2011 | 0.528 | 0.617 | 0.606 | 0.422 | 0.426 | 0.365 |
2012 | 0.531 | 0.616 | 0.610 | 0.420 | 0.423 | 0.362 |
2013 | 0.535 | 0.616 | 0.614 | 0.417 | 0.428 | 0.366 |
2014 | 0.516 | 0.591 | 0.599 | 0.404 | 0.421 | 0.366 |
2015 | 0.534 | 0.607 | 0.616 | 0.404 | 0.427 | 0.374 |
2016 | 0.526 | 0.587 | 0.610 | 0.415 | 0.431 | 0.408 |
2017 | 0.542 | 0.603 | 0.621 | 0.432 | 0.444 | 0.429 |
2018 | 0.528 | 0.591 | 0.614 | 0.427 | 0.444 | 0.430 |
2019 | 0.507 | 0.574 | 0.596 | 0.426 | 0.445 | 0.430 |
均值 | 0.543 | 0.626 | 0.632 | 0.429 | 0.442 | 0.383 |
Table 3 Source decomposition of carbon emission efficiency difference in resource-based cities表3 资源型城市碳排放效率差异来源分解 |
年份 | 区域内 | 贡献率/% | 区域间 | 贡献率/% | 超变密度 | 贡献率/% |
---|---|---|---|---|---|---|
2004 | 0.157 | 31.24 | 0.245 | 48.73 | 0.101 | 20.03 |
2005 | 0.153 | 30.89 | 0.241 | 48.62 | 0.102 | 20.49 |
2006 | 0.152 | 30.99 | 0.235 | 47.99 | 0.103 | 21.02 |
2007 | 0.156 | 31.28 | 0.235 | 47.17 | 0.108 | 21.55 |
2008 | 0.151 | 30.48 | 0.241 | 48.64 | 0.104 | 20.88 |
2009 | 0.153 | 31.27 | 0.230 | 47.06 | 0.106 | 21.67 |
2010 | 0.146 | 31.22 | 0.210 | 44.96 | 0.111 | 23.82 |
2011 | 0.147 | 31.77 | 0.202 | 43.66 | 0.114 | 24.58 |
2012 | 0.147 | 31.72 | 0.203 | 43.99 | 0.112 | 24.30 |
2013 | 0.147 | 31.61 | 0.205 | 44.04 | 0.113 | 24.35 |
2014 | 0.143 | 31.67 | 0.194 | 43.05 | 0.114 | 25.28 |
2015 | 0.143 | 31.07 | 0.202 | 43.88 | 0.115 | 25.05 |
2016 | 0.142 | 30.88 | 0.190 | 41.44 | 0.127 | 27.68 |
2017 | 0.147 | 30.93 | 0.191 | 40.21 | 0.137 | 28.85 |
2018 | 0.145 | 31.10 | 0.187 | 40.08 | 0.134 | 28.83 |
2019 | 0.146 | 31.87 | 0.177 | 38.58 | 0.135 | 29.55 |
均值 | 0.148 | 31.25 | 0.212 | 44.51 | 0.115 | 24.25 |
Table 4 The description of influencing factors and variables of carbon emission efficiency difference in resource-based cities表4 资源型城市碳排放效率差异的影响因素及变量说明 |
变量名称 | 变量含义 | 指标计算 | 数据来源 |
---|---|---|---|
Pop | 人口密度差异 | 城市i和城市j户籍人数与行政区域面积比值差值矩阵 | 中国城市统计年鉴 |
Ps | 人口结构差异 | 城市i和城市j中小学生人数占户籍人口比例差值矩阵 | 中国区域经济统计年鉴 |
Dpi | 居民可支配收入差异 | 城市i和城市j城镇居民可支配收入差值矩阵 | 中国区域经济统计年鉴 |
Fis | 财政支出差异 | 城市i和城市j财政支出占GDP比例差值矩阵 | 中国城市统计年鉴 |
Road | 基础设施建设差异 | 城市i和城市j公路里程与行政区域面积比值差值矩阵 | 中国区域经济统计年鉴 |
Co | 规模企业数量差异 | 城市i和城市j规模以上工业企业数量差值矩阵 | 中国城市统计年鉴 |
Table 5 Benchmark regression results表5 基准回归结果 |
变量名称 | 2004年 | 2009年 | 2014年 | 2019年 |
---|---|---|---|---|
Intercept | -0.272*** | -0.261*** | -0.312*** | -0.219*** |
Pop | 1.132** | 1.432** | 1.256** | 3.028*** |
Ps | 0.051 | 0.286 | -0.304 | -0.525* |
Dpi | 0.017** | 0.006 | 0.123*** | 0.021*** |
Fis | 0.108 | 0.050 | -0.494 | -0.035 |
Road | -0.027 | -0.093** | -0.045 | -0.114*** |
Co | 0.059*** | 0.064*** | 0.054*** | 0.041*** |
R2 | 0.194 | 0.231 | 0.215 | 0.195 |
随机置换次数/次 | 5000 | 5000 | 5000 | 5000 |
Table 6 Heterogeneous regression results表6 异质性回归结果 |
变量名称 | 再生型 | 成熟型 | 成长型 | 衰退型 |
---|---|---|---|---|
Intercept | -0.056*** | -0.095*** | -0.217*** | 0.036*** |
Pop | 4.928** | 5.182*** | 6.978** | -1.159** |
Ps | -0.353** | -0.577 | 0.710 | 0.171 |
Dpi | 0.019 | 0.017** | 0.017 | -0.008 |
Fis | 0.124 | 0.092 | -0.159 | -0.186 |
Road | -0.309** | -0.249*** | -0.134*** | -0.254*** |
Co | 0.037* | 0.021*** | 0.030*** | 0.016** |
R2 | 0.175 | 0.213 | 0.381 | 0.338 |
随机置换次数/次 | 5000 | 5000 | 5000 | 5000 |
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资源型城市作为一类特殊城市,发展过程具有明显的阶段性特征。本文以灯光影像等数据为基础,采用门槛面板模型、非参数估计等方法,从城市空间结构演化角度,对资源型城市发展阶段进行定量划分,并分析了不同发展阶段中心城区空间集聚对城市经济增长、城市转型的差异性特征,揭示了资源型产业对城市空间结构演变的作用以及资源型城市转型关键时间节点和政策着力点。结果表明:资源型城市空间相对分散,但不同资源类型城市之间存在较大差异;采掘业从业人员占比1.9%和31.0%可以作为资源型城市不同发展阶段的重要标志性指标;不同发展阶段中,社会经济要素在中心城区的集聚程度与城市经济增长的相关关系明显不同,成熟发展期成为资源型城市转型发展的重要转折期。
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Resource-based cities refer to the cities where the exploiting and processing of natural resources, such as minerals and forests, dominate industrial development. This special type of city is obviously staged during its development. From a new perspective of urban spatial structure, this paper quantitatively splits the development stages of resource-based cities based on the data of night lights images and uses the methods, such as the threshold panel model and non-parametric estimation. To delve into the impacts of resource-based industries on the evolution of urban spatial structure, as well as the timing of transformation policy design, different effects of the spatial agglomeration of central urban areas on urban growth and transformation by development stages are also analyzed in this paper. The results suggest that: the resource-based cities are relatively internally fragmented with noticeable differences among resource types and individuals, such as oil and gas resource type, and ferrous metal resource type. This paper attempts to adopt the proportion of mining employees as the indicator. Some 1.9% and 31.0% can act as the tipping points to divide different development stages of resource-based cities. The resource-based cities are split into four stages of development given two threshold values. In various stages of development, the correlation between the concentration of social and economic factors in the central urban area and the urban economic growth is noticeably different. Furthermore, 'the mature stage' has acted as a critical turning phase in the transformation of resource-based cities. {{custom_citation.content}}
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城市能源消费碳排放核算方法的选择对核算结果具有一定影响。本研究参照IPCC温室气体排放清单编制方法,根据中国能源统计现状,利用能源表观消费量数据和现行的能源消费碳排放核算方法,将能源消费碳排放核算方法分为3种核算方式:①基于能源平衡表的能源消费碳排放核算;②基于一次能源消费量的能源消费碳排放核算;③基于终端能源消费量的能源消费碳排放核算,并分别根据3种能源消费核算方法构建城市能源消费碳排放核算体系;以北京市为案例对比3种方法的能源消费碳排放核算结果。研究结果表明,能源消费碳排放核算方法的选择对核算结果有很大影响,通过分析误差产生的原因,认为排放因子、碳氧化水平及加工转换过程是产生不确定性的3个主要原因。基于能源平衡表的修正后的能源消费碳排放核算方法,可以在一定程度上避免在能源加工转换过程中的二次能源消费遗漏及重复计算,然而,由于当前的国民经济核算体系尚不能满足能源消费碳排放计算的需要,需要尽快建立更为细致的统计核算体系。
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Energy-related CO<sub>2</sub> emission is one of the largest drivers for accelerating atmosphere radiative forcing which contribute to global warming, and thus needs to be taken in high priority for Greenhouse-gas (GHG) emission abatement. Cities, with its aggregation of economic activities and associated energy use, are the main contributors of energy consumption CO<sub>2</sub> emissions and may be critical for CO<sub>2</sub> mitigation and adaptation to climate change. However, uncertainty exists among urban energy consumption CO<sub>2</sub> emissions, as the accounting methods of carbon emissions for urban energy consumption would affect accounting results, hindering climate adaptation and CO<sub>2</sub> mitigation policies. Here, we analyzed the discrepancy between different CO<sub>2</sub> accounting methods, and illustrated the underlying reasons. The study is based on the reference approach of IPCC Guideline for National Greenhouse-gas Emission Inventory, adopted China’s energy statistical data, and categorized energy consumption into three accounting methods based on apparent energy consumption data, i.e., 1) final energy use of carbon emission from an energy input-output perspective, 2) total energy consumption account by major energy category, and 3) final use account by energy category. Solid, liquid, and gas fuel are accounted for. Furthermore, an accounting system of carbon emissions from urban energy consumption was established based on the three types of accounting methods. Accounting results of carbon emission from energy consumption in Beijing were obtained with the three methods. Results show that discrepancies exist among the three types of accounting methods. By further analyzing the causes of discrepancy, it was concluded that 1) the discrepancy is generally caused by differing account methods and associated data error; 2) the accounting method of CO<sub>2</sub> emissions based on the energy balance sheet may avoid the omission and double counting of the secondary energy consumption due to its direct reflection on the input and output status of energy production and use, and lead to a smaller error compared with other methods in theory. It was indicated that the accounting method based on the energy balance sheet needs to be placed in high priority for urban energy consumption CO<sub>2</sub> emission accounting. The current statistics system needs to be more explicit to satisfy the calculation requirement of carbon emissions from energy consumption.
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程叶青, 王哲野, 张守志, 等. 中国能源消费碳排放强度及其影响因素的空间计量. 地理学报, 2013, 68(10): 1418-1431.
碳排放所引起的全球气候变化对人类经济社会发展带来了严峻的挑战。中国政府承诺到2020 年GDP碳排放强度较2005 年降低40%~45%,这一目标的实现有赖于全国层面社会经济和产业结构的实质性转型,更有赖于省区层面节能减排的具体行动。基于联合国政府间气候变化专门委员会(IPCC) 提供的方法,本文估算了全国30 个省区1997-2010 年碳排放强度,采用空间自相关分析方法和空间面板计量模型,探讨了中国省级尺度碳排放强度的时空格局特征及其主要影响因素,旨在为政府制定差异化节能减排的政策和发展低碳经济提供科学依据。研究结果表明:① 1997-2010 年,中国能能源消费CO<sub>2</sub>排放总量从4.16 Gt 增加到11.29Gt,年均增长率为7.15%,而同期GDP年均增长率达11.72%,碳排放强度总体上呈逐年下降的态势;② 1997-2010 年,碳排放强度的Moran's I 指数呈波动型增长,说明中国能源消费碳排放强度在省区尺度上具有明显的空间集聚特征,且集聚程度有不断增强的态势,同时,碳排放强度高值集聚区和低值集聚区表现出一定程度的路径依赖或空间锁定;③ 空间面板计量模型分析结果表明,能源强度、能源结构、产业结构和城市化率对中国能源消费碳排放强度时空格局演变具有重要影响;④ 提高能源利用效率,优化能源结构和产业结构,走低碳城市化道路,以及实行节能减排省区联动策略是推动中国实现节能减排目标的重要途径。
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The economic and social development has been facing with serious challenge brought by global climate change due to carbon emissions. As a responsible developing country, China pledged to reduce its carbon emission intensity by 40%-45% below 2005 levels by 2020. The realization of this target depends on not only the substantive transition of society, economy and industrial structure in national scale, but also the specific action and share of energy saving and emissions reduction in provincial scale. Based on the method provided by the IPCC, this paper examines the spatio-temporal dynamic patterns and domain factors of China's carbon emission intensity from energy consumption in 1997-2010 using spatial autocorrelation analysis and spatial panel econometric model. The aim is to provide scientific basis for making different policies on energy conservation and carbon emission reduction in China. The results are shown as follows. Firstly, China's carbon emissions increased from 4.16 Gt to 11.29 Gt in 1997-2010, with an annual rate of 7.15%, which was much slower than that of annual growth rate of GDP (11.72%); therefore, China's carbon emission intensity tended to decline. Secondly, the changing curve of Moran's I indicated that China's carbon emission intensity from energy consumption has a continued strengthening tendency of spatial agglomeration at provincial scale. The provinces with higher and lower values appeared to be path-dependent or space-locked to some extent. Third, according to the analysis of spatial panel econometric model, it can be found that energy intensity, energy structure, industrial structure and urbanization rate were the domain factors that have impact on the spatio-temporal patterns of China's carbon emission intensity from energy consumption. Therefore, in order to realize the targets of energy conservation and emission reduction, we should improve the utilizing efficiency of energy, and optimize energy and industrial structure, and choose the low-carbon urbanization way and implement regional cooperation strategy of energy conservation and emissions reduction.
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耿文欣, 范英. 碳交易政策是否促进了能源强度的下降? 基于湖北试点碳市场的实证. 中国人口·资源与环境, 2021, 31(9): 104-113.
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王少剑, 高爽, 黄永源, 等. 基于超效率SBM模型的中国城市碳排放绩效时空演变格局及预测. 地理学报, 2020, 75(6): 1316-1330.
由CO<sub>2</sub>排放所引起的气候变化是当今社会所关注的热点话题,提高碳排放绩效是碳减排的重要途径。目前关于碳排放绩效的研究多从国家尺度和行业尺度进行探讨,由于能源消耗统计数据有限,缺乏城市尺度的研究。基于遥感模拟反演的1992—2013年中国各城市碳排放数据,采用超效率SBM模型对城市碳排放绩效进行测定,构建马尔可夫和空间马尔可夫概率转移矩阵,首次从城市尺度探讨了中国碳排放绩效的时空动态演变特征,并预测其长期演变的趋势。研究表明,中国城市碳排放绩效均值呈现波动中稳定上升的趋势,但整体仍处于较低的水平,未来城市碳排放绩效仍具有较大的提升空间,节能减排潜力大;全国城市碳排放绩效空间格局呈现“南高北低”特征,城市间碳排放绩效水平的差异性显著;空间马尔科夫概率转移矩阵结果显示,中国城市碳排放绩效类型转移具有稳定性,且存在“俱乐部收敛”现象,地理背景在中国城市碳排放绩效类型转移过程中发挥重要作用;从长期演变的趋势预测来看,中国碳排放绩效未来演变较为乐观,碳排放绩效随时间的推移而逐步提升,碳排放绩效分布呈现向高值集中的趋势。因此未来中国应继续加大节能减排力度以提高城市碳排放绩效,实现国家节能减排目标;同时不同地理背景的邻域城市之间应建立完善的经济合作联动机制,以此提升城市碳排放绩效水平并追求经济增长与节能减排之间协调发展,从而实现低碳城市建设和可持续发展。
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Climate change caused by CO2 emissions has become an environmental issue globally in recent years, and improving carbon emission performance is an important way to reduce carbon emissions. Although some scholars have discussed the carbon emission performance at the national scale and industry level, literature lacks studies at the city- level due to a limited availability of statistics on energy consumptions. In this study, based on China's city-level remote sensing carbon emissions from 1992 to 2013, we used the super-efficiency SBM model to measure the urban carbon emission performance, and the traditional Markov probability transfer matrix and spatial Markov probability transfer matrix are constructed to explore the spatio-temporal dynamic evolution characteristics of urban carbon emission performance in China for the first time and to predict its long-term evolution trend. The study shows that urban carbon emission performance in China presents a trend of steady increase in the fluctuation, but the overall level is still at a low level, so there is still a great improvement space in urban carbon emission performance, with huge potential for energy conservation and emission reduction. The spatial pattern of national urban carbon emission performance shows the characteristics of "high in the south and low in the north", and there is a significant difference in the level of carbon emission performance between cities. The spatial Markov probabilistic transfer matrix results show that the transfer of carbon emission performance type in Chinese cities is stable, thus it forms the "club convergence" phenomenon, and the geographical background plays an important role in the process of the transfer. From the perspective of long-term trend prediction, the future evolution of urban carbon emission performance in China is relatively optimistic. The carbon emission performance will gradually improve over time, and the distribution of carbon emission performance presents a trend of high concentration. Therefore, in the future, China should continue to strengthen research and development to improve the performance level of urban carbon emissions and achieve the national target of energy conservation and emission reduction. At the same time, neighboring cities with different geographical backgrounds should establish a sound linkage mechanism of economic cooperation to pursue coordinated development between economic growth, energy conservation and emission reduction, so as to realize low-carbon city construction and sustainable development. {{custom_citation.content}}
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[9] |
李金铠, 马静静, 魏伟. 中国八大综合经济区能源碳排放效率的区域差异研究. 数量经济技术经济研究, 2020, 37(6): 109-129.
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[10] |
王凯, 张淑文, 甘畅, 等. 中国旅游业碳排放效率的空间网络结构及其效应研究. 地理科学, 2020, 40(3): 344-353.
基于中国30个省区2001-2016年面板数据,采用SBM模型测度各省区旅游业碳排放效率,借助修正的引力模型和社会网络分析方法,厘清中国旅游业碳排放效率的空间网络结构及其效应。研究表明:① 中国旅游业碳排放效率的空间关联渐趋紧密,网络发育程度日益完善,但距理想状态仍有差距;② 各省区网络中心性指标分异性逐步减小,上海、北京、江苏等省区排名稳居前列,重庆、福建、内蒙古等省区排名波动上升,宁夏、青海、山西等省区排名相对滞后;③ 网络整体呈核心区由东部沿海向中部及西南地区持续扩展,而边缘区范围逐步收缩态势;④ 网络密度与旅游业碳排放效率呈正相关,与旅游业碳排放效率差异构成负相关关系,网络等级度和网络效率则与之相反,网络中心性各指标的提升均能显著增强旅游业碳排放效率。
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[11] |
胡剑波, 闫烁, 韩君. 中国产业部门隐含碳排放效率研究: 基于三阶段DEA模型与非竞争型I-O模型的实证分析. 统计研究, 2021, 38(6): 30-43.
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盖美, 朱静敏, 孙才志, 等. 中国沿海地区海洋经济效率时空演化及影响因素分析. 资源科学, 2018, 40(10): 1966-1979.
为展现中国沿海地区海洋经济效率时空演变现状,并根据其具体原因提出针对性对策建议,促进中国海洋经济可持续发展,本文基于考虑非期望产出的三阶段超效率SBM-Global模型、标准差椭圆和重心坐标方法,对中国沿海11个省市2000—2015年海洋经济效率进行测算和时空对比分析。研究发现:①与一阶段相比,三阶段海洋经济效率整体上升,各省市排名出现不同程度变动;②时间演变上,全国各沿海地区海洋经济效率呈上升趋势,绝对差异和相对差异都不断变大;③空间演变上,全国标准差椭圆呈东北-西南格局分布,面积由小变大,效率分布由收缩趋势变为分散趋势,最终呈北、中、南三级格局分布,与三大海洋经济圈分布相吻合;三大海洋经济圈标准差椭圆面积都逐渐缩小,效率分布出现极化现象;④海洋经济区位熵、海洋科研人力资本、海洋环保技术水平与海洋经济效率之间存在非线性关系;陆域经济发展水平、海洋产业结构水平、政府对海洋科技支持力度、海洋经济政策力度对海洋经济效率呈显著正影响;对外开放水平对其影响不显著。
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Based on the three stage super-efficient SBM-Global model, standard deviation ellipse and centroid coordinate method, the marine economic efficiency of 11 coastal provinces and cities of China in 2000-2015 years is calculated and analyzed with the time and space. Research discovery: ① Compared with the first stage, the efficiency of marine economy in the third stage has increased overall, and the ranking of provinces and cities has changed differently. ② In the evolution of time: The efficiency of marine economy in all coastal areas of China has been on the rise, and the absolute and relative difference has been increasing and the relative difference changes relatively smooth. ③ In space evolution: The national standard deviation ellipse was distributed in the Northeast-Southwest pattern and its area increased from small to large. The efficiency distribution changes from the contraction trend to the dispersion trend and finally in the three level pattern of North, Middle and South, which is in accordance with the three major marine economic circles. The standard deviation ellipse area of the three major marine economic circles is gradually reduced, and polarization appears in the efficiency distribution. ④ There is a nonlinear relationship between development level of land area economy, marine economic location entropy, human capital of marine scientific research, marine environmental protection technology level and marine economic efficiency; land economic development level, the level of marine industrial structure, the government's support for marine science and technology has a significant positive impact on it, and the level of opening to the outside world has no significant impact on it. {{custom_citation.content}}
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李博, 张文忠, 余建辉. 碳排放约束下的中国农业生产效率地区差异分解与影响因素. 经济地理, 2016, 36(9): 150-157.
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张卓群, 张涛, 冯冬发. 中国碳排放强度的区域差异、动态演进及收敛性研究. 数量经济技术经济研究, 2022, 39(4): 67-87.
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屈小娥. 中国省际全要素CO2排放效率差异及驱动因素: 基于1995—2010年的实证研究. 南开经济研究, 2012, 28(3): 128-141.
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莫惠斌, 王少剑. 黄河流域县域碳排放的时空格局演变及空间效应机制. 地理科学, 2021, 41(8): 1324-1335.
利用空间面板模型、空间自相关分析和以区域背景与最近邻状况为空间滞后的空间马尔科夫链对2000—2017年黄河流域县域碳排放时空格局与空间效应进行分析,结果表明:① 2000年以来黄河流域碳排放量激增,由山东全域和陕甘宁蒙交界的高值区向外圈层与轴向扩张,形成东高西低碳排放格局;② 存在“俱乐部趋同”现象,高碳排放县集聚于山东全域和陕甘宁蒙交界,低碳排放县集聚于西南部;2000年与2017年对比发现县域碳排放类型稳定性强,较高碳排放变为较低碳排放的县集中在东南部区域,而相反方向转变的县集中在内蒙古;③ 高碳溢出效应与低碳锁定效应是塑造时空格局的重要作用力,前者作用力更强;区域背景增强了“俱乐部趋同”与被包围异常值趋同,作用力强于最近邻状况,不显著区域内碳排放类型转变概率提高。④ 空间面板模型结果显示年轻人口结构、大经济规模、二产为主产业结构、高生活水平和高公共支出促进了碳排放量增加与空间效应作用,其中经济规模与产业结构是重要驱动因素。
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Carbon emission control is the main problem and measure of ecological protection and high-quality development in the Yellow River Basin. Carbon emission at county level research can provide more accurate theoretical support for collaborative governance and sustainable development of the Yellow River Basin. Spatial panel model, spatial autocorrelation analysis and spatial Markov chain with regional background and nearest neighbor as spatial lags were used to analyze the spatiotemporal pattern and spatial effect of carbon emissions in counties of the Yellow River Basin from 2000 to 2017, the results showed that: 1) the carbon emission in the Yellow River basin has increased dramatically since 2000; the high carbon emissions areas, Shandong province and the boundary between Shaanxi, Gansu, Ningxia and Inner Mongolia, expands to the outer circle layer and the axial direction, forming the spatial pattern of high in the east and low in the west; 2) there is a phenomenon of “club convergence”; the high carbon emission counties converge in Shandong province and the boundary between Shaanxi, Gansu, Ningxia and Inner Mongolia; the low carbon emission counties converge in the southwest; the comparison between 2000 and 2017 shows that county carbon emission type has strong stability; counties which tranfered from higher carbon emission type to lower carbon emission type are concentrated in the southeast region, while counties that change in the opposite direction are concentrated in Inner Mongolia. 3) high carbon spillover effect and low carbon locking effect are important forces to shape the spatiotemporal pattern and the former is stronger; the regional background enhances “club convergence” and the convergence of surrounded outliers and its acting force was stronger than the nearest neighbor; the probability of carbon emission type transition in insignificant regions increased; 4) the spatial panel model shows that increase of carbon emissions and its spatial effect are promoted by young population structure, large economy, industrial structure dominated by the secondary industry, high living standard and high public expenditure; economy and industrial structure are important driving factors. {{custom_citation.content}}
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李顺成, 肖卫东, 王志宝. 家庭部门能源消费影响因素及碳排放结构研究: 基于PLS结构方程模型的实证解析. 软科学, 2020, 34(2): 117-123.
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刘志华, 徐军委, 张彩虹. 科技创新、产业结构升级与碳排放效率: 基于省际面板数据的PVAR分析. 自然资源学报, 2022, 37(2): 508-520.
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禹湘, 陈楠, 李曼琪. 中国低碳试点城市的碳排放特征与碳减排路径研究. 中国人口·资源与环境, 2020, 30(7): 1-9.
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邵海琴, 王兆峰. 中国交通碳排放效率的空间关联网络结构及其影响因素. 中国人口·资源与环境, 2021, 31(4): 32-41.
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周杰琦, 韩颖, 林洪. FDI对中国工业碳排放效率的影响机理及其效应: 理论构建与经验分析. 软科学, 2016, 30(1): 76-80.
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朱德进, 杜克锐. 对外贸易、经济增长与中国二氧化碳排放效率. 山西财经大学学报, 2013, 35(5): 1-11.
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何文举, 张华峰, 陈雄超, 等. 中国省域人口密度、产业集聚与碳排放的实证研究: 基于集聚经济、拥挤效应及空间效应的视角. 南开经济研究, 2019, 35(2): 207-225.
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李在军, 尹上岗, 姜友雪, 等. 长三角经济增长与碳排放异速关系及形成机制. 自然资源学报, 2022, 37(6): 1507-1523.
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王睿, 张赫, 强文丽, 等. 基于城镇化的中国县级城市碳排放空间分布特征及影响因素. 地理科学进展, 2021, 40(12): 1999-2010.
论文选择中国1897个县级城市作为研究单元,基于CHRED-online碳排放公开数据库以及县、县级市社会经济统计数据,采用空间自相关分析和地理探测器方法,探究中国县级城市碳排放空间分布格局及人口、经济、土地多维度城镇化水平对碳排放的影响。结果表明:① 中国县级城市碳排放量非均衡性较高,碳排放总量高值地区数量少,但数值较大。② 碳排放总量空间分布主要呈现东高西低格局,高值地区主要集中于东部、中部大城市周边和内蒙古中部、北部地区,呈“簇状”分布结构。人均碳排放强度和经济碳排放强度则呈现北高南低格局,主要聚集于内蒙古中部、北部和新疆青海交界地区。③ 经济和土地城镇化水平的空间异质性对县级城市碳排放总量差异具有较强的解释力,人口城镇化对碳排放总量影响不明显。经济城镇化及土地城镇化各指标之间交互作用对碳排放影响最为剧烈,并呈现非线性增强作用。④ 在分地区差异性比较中,城镇化水平对西部欠发达地区影响作用最为剧烈。在同一指标的解释力和关键影响因素指标的选取方面,东、中、西部地区也存在明显的空间分异特征。应结合高碳排放区域和城镇化影响作用机制,进行差异化控碳路径选择。
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This study selected 1897 county-level cities in China as the research unit to examine the spatial distribution pattern of carbon emissions and the effects of population, economy, and multi-dimensional urbanization levels on carbon emissions. The data sources are CHRED-online carbon emission public database and social and economic statistics of counties and county-level cities. Spatial autocorrelation and geographic detector methods were used in the empirical research. The results show that: 1) County-level cities of China are relatively highly different in carbon emissions, and the number of areas with high carbon emissions is relatively small, but the emission value is relatively large. The carbon emissions per unit of GDP are higher than the national average. 2) The spatial distribution of total carbon emissions mainly presents a pattern of high in the east and low in the west. The high-value areas of total carbon emissions are mainly concentrated in the east, the periphery of large cities in central China, and the central and northern regions of Inner Mongolia, showing a clustered distribution structure. Per capita carbon emissions and carbon intensity show a pattern of high in the north and low in the south, mainly concentrated in the central and northern parts of Inner Mongolia and the border areas of Xinjiang and Qinghai. 3) The spatial heterogeneity of the level of economic and land urbanization has a strong explanatory power for the difference in the total carbon emissions of county-level cities, and the impact of population urbanization on the total carbon emissions is not obvious. The interaction between economic urbanization and land urbanization has the most dramatic impact on carbon emissions, and shows a nonlinear enhancement effect. 4) With regard to regional differences, the level of urbanization has the most dramatic effect on the underdeveloped areas in the west. The eastern, central, and western regions also show different spatial characteristics in terms of the explanatory power of the same indicators and the key influencing factors. {{custom_citation.content}}
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孙亚男, 杨名彦. 中国绿色全要素生产率的俱乐部收敛及地区差距来源研究. 数量经济技术经济研究, 2020, 37(6): 47-69.
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张军, 吴桂英, 张吉鹏. 中国省际物质资本存量估算: 1952—2000. 经济研究, 2004, 39(10): 35-44.
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张少辉, 余泳泽, 杨晓章. 中国城市固定资本存量估算与生产率收敛分析: 1988—2015. 中国软科学, 2021, 36(7): 74-86.
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柯善咨, 向娟. 1996—2009年中国城市固定资本存量估算. 统计研究, 2012, 29(7): 19-24.
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韩刚, 袁家冬, 张轩, 等. 紧凑城市空间结构对城市能耗的作用机制: 基于江苏省的实证研究. 地理科学, 2019, 39(7): 1147-1154.
以江苏省为研究对象,选取2007~2016年数据资料,探讨城市紧凑发展与能耗间的作用关系。首先计算城市紧凑度和能耗数值,紧凑度通过构建评价指标体系测算,包括土地利用、经济、人口、基础设施、公共服务5个一级指标以及12个二级指标,城市能源消耗以全社会用电总量、天然气供气总量、液化石油气供气总量折算标准煤方式获取。其次,将江苏省城市紧凑度和能耗数值进行空间可视化,观察两者在4个时间节点,地域和时空上的演变。最后,运用灰色关联分析法和计量经济学中的普通最小二乘法及一阶差分广义矩估计,对城市紧凑度与能耗的相关关系、作用机制进行定量研究。通过研究发现,江苏省城市紧凑度和能耗皆存在空间上的集聚分布特征,且两者具有较强的相关性,紧凑度的提升对能耗有显著的抑制作用,但这种作用表现出较为典型的时效性和滞后性,城市空间结构调整所带来的效用增长会缓慢作用于能源利用结构。
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The concept of compact city provides an efficient urban development model with a fine fabric and organic connection between urban spatial structures. It achieves intensive land utilization pattern by controlling the urban sprawl with urban growth boundary, as well as the linked communities by the advanced public transportation system. In addition, as one of the most recommendable urban development ways advocated by sustainability science, the greatest value of compact city is considered to reduce energy consumption significantly in the process of urban economic and social development. Taking Jiangsu Province as an example, this study selects mass of statistical data from 2007 to 2016, to explore whether a compact city can contribute to reducing power consumption and emission, and testing the quantitative relationship, how does compact urban structure affect energy consumption as well its force direction. Firstly, the urban compactness and energy consumption value are calculated. An assessment index system with five primary indicators (land utilization, economy, population, infrastructure, and public services) and twelve secondary indicators is built, and the urban compactness is measured by the value of these indicators. It should be noted that, subject to the limitation of the complexity of urban energy consumption statistics in China, energy consumption value is obtained by converting the original row data of the whole society’s electricity, total natural gas supply and total liquefied petroleum gas (LPG) supply into the standard coal then adding them together. Such processing method can greatly reduce the error of results. Secondly, the representation of urban compactness and energy consumption value are visualized spatially by prefecture-level city, and then the temporal and spatial evolution of these two variables are observed at four time points. Finally, the correlation between urban compactness and energy consumption is investigated by employing the grey relational analysis method, and the interactive mechanism of them is quantified by using the ordinary least squares method and the first-order difference generalized moment estimation in econometrics. This study finds that both urban compactness and energy consumption in Jiangsu Province have significant characteristics of spatial concentration, and the northern and southern parts of Jiangsu are significantly different by taking the Yangtze River as the boundary. Moreover, urban compactness and energy consumption are strongly correlated. The increase in compactness promotes its inhibitory effect on energy consumption, however, this effect shows typical timeliness and hysteresis. The utility of adjusting urban spatial structure will conduce to the changes of energy utilization structure gradually. Clearly, the results show that compact city plays an important role in the transformation process in terms of urban spatial development from incremental expansion to inventory optimization in the eastern China. Besides, from the perspective of practical application in spatial planning, compact city enhances the integration of production, living and ecological spaces in urban area, and promotes the sustainable and coordinated development. {{custom_citation.content}}
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