Provincial Spatial Network Structure of Carbon Emissions from Service Industry and Driving Factors in China
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摘要: 在碳达峰、碳中和的时代背景下，探索服务业碳排放空间网络结构及其驱动因素对于推进服务业节能减排具有重要的实践价值. 基于2000—2018年中国省际服务业碳排放的面板数据(不含西藏自治区及港澳台地区数据)，综合运用修正后的引力模型和社会网络分析法刻画服务业碳排放空间网络结构特征并厘清其驱动因素. 结果表明：①研究期内中国服务业碳排放空间联系强度不断增大，服务业碳排放空间网络结构趋于复杂化且稳定性显著提升. ②上海市、北京市、江苏省和浙江省等省份在中国服务业碳排放空间网络结构中扮演“中心行动者”的角色，这些省份是其他省份进行服务业碳排放空间关联的重要“桥接”和“枢纽”，控制与主导中国服务业碳排放的空间关联和空间溢出. ③省份间的地理位置越邻近，产业结构优化、城镇化水平和科技发展水平的差异越大，中国服务业碳排放空间的联系越多；而服务业碳排放强度的差异越大，服务业碳排放空间的联系越少. 研究显示，中国省际服务业碳排放存在空间关联和空间溢出，但空间网络结构仍较为松散，未来在推进服务业节能减排的工作中，需重视建立省际协同减排机制.Abstract: In the era of carbon emission peak and carbon neutrality, it is of great practical value for promoting the energy conservation and emission reduction in the service industry to systematically explore the spatial network structure and its driving factors of carbon emissions from service industry. Based on the provincial panel data of carbon emissions (excluding the data of Tibet Autonomous Region, Hongkong, Macon and Taiwan) in China's service industry from 2000 to 2018, the modified gravity model and social network analysis (SNA) were used to analyze the characteristics of spatial network structure of carbon emissions in service industry and clarify the driving factors. The main results are as follows. Firstly, the spatial connection strength of carbon emissions in the service industry continued to increase. Additionally, the spatial network structure of carbon emissions in the service industry became increasingly complicated and stable. Secondly, Shanghai, Beijing, Jiangsu Province, Zhejiang Province, and other provinces played a role of ‘central actor’ in the spatial network structure of carbon emissions in China's service industry. Specifically, the above-mentioned provinces were also important ‘bridges’ for other provinces to implement network connections of carbon emissions in the service industry. Additionally, the ‘hub’ controls and dominates the spatial connection and the spatial spillover of carbon emissions from service industry in China. Thirdly, the closer the geographical location of provinces, the greater the differences in the optimization of industrial structure, the level of urbanization, and the ability of technological innovation, the more spatial connections of carbon emissions from service industry in China. On the contrary, the greater the difference in carbon emission intensity of the service industry, the less spatial connections of carbon emissions in the service industry. The research shows that there were spatial correlations and spatial spillover of carbon emissions in China's inter-provincial service industry, but the spatial network structure was still relatively loose. In the promotion of energy conservation and carbon emission reduction in the service industry, a synergistic inter-provincial emission reduction mechanism needs to be established.
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图 1 中国服务业碳排放空间联系强度(排名前30位的省份)
注：弧线与圆的接触面积上的宽度表示省份之间的关系程度或比例关系，省份间的线条粗细程度表征服务业碳排放空间联系强弱.
Figure 1. Spatial connection strength of carbon emission from service industry in China (top 30 provinces)
图 2 中国服务业碳排放整体空间网络结构特征
Figure 2. Overall spatial network structure characteristics regarding carbon emission of service industry in China
表 1 能源消费类型及碳排放系数
Table 1. Type of energy consumption, and coefficient of carbon emission
能源消费类型 碳排放系数 能源消费类型 碳排放系数 煤炭 1.98 柴油 3.16 焦煤 3.04 燃料油 3.24 原油 3.07 天然气 2.18 汽油 3.01 电力 6.2×10−4 煤油 3.1 注：电力的碳排放系数单位为t/(kW·h)，其他能源类别的碳排放系数无单位. 
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表 2 2018年中国服务业碳排放个体空间网络结构特征
Table 2. Individual spatial network structure characteristics regarding carbon emission from service industry in China in 2018
省份 度数中心度 接近中心度 中间中心度 点入度 点出度 总度数 中心度 排名 中心度 排名 中心度 排名 北京市 26 7 33 89.655 2 90.625 2 24.263 1 天津市 12 4 16 44.828 6 64.444 6 3.964 5 河北省 3 4 7 13.793 24 53.704 24 0.030 28 山西省 1 5 6 17.241 19 54.717 19 0.067 23 内蒙古自治区 1 2 3 6.897 30 51.786 30 0.010 30 辽宁省 1 3 4 10.345 29 52.727 29 0.030 28 吉林省 0 4 4 13.793 24 53.704 24 0.067 23 黑龙江省 0 4 4 13.793 24 53.704 24 0.067 23 上海市 27 7 34 93.103 1 93.548 1 24.188 2 江苏省 22 4 26 75.862 3 80.556 3 12.979 3 浙江省 15 2 17 51.724 4 67.442 4 4.633 4 安徽省 3 4 7 13.793 24 53.704 24 0.056 27 福建省 0 6 6 20.690 11 55.769 11 0.097 17 江西省 2 5 7 20.690 11 55.769 11 0.157 14 山东省 7 5 12 27.586 7 58.000 7 0.508 7 河南省 5 5 10 24.138 8 56.863 8 0.382 9 湖北省 2 5 7 17.241 19 54.717 19 0.095 18 湖南省 3 5 8 20.690 11 55.769 11 0.157 14 广东省 11 7 18 48.276 5 65.909 5 3.251 6 广西壮族自治区 2 5 7 20.690 11 55.769 11 0.095 18 海南省 0 6 6 20.690 11 55.769 11 0.095 18 重庆市 3 5 8 20.690 11 55.769 11 0.161 13 四川省 1 6 7 20.690 11 55.769 11 0.226 12 贵州省 2 5 7 17.241 19 54.717 19 0.090 22 云南省 0 5 5 17.241 19 54.717 19 0.095 18 陕西省 0 5 5 17.241 19 54.717 19 0.150 16 甘肃省 0 7 7 24.138 8 56.863 8 0.382 9 青海省 0 7 7 24.138 8 56.863 8 0.493 8 宁夏回族自治区 0 4 4 13.793 24 53.704 24 0.067 23 新疆维吾尔自治区 0 6 6 20.690 11 55.769 11 0.239 11 平均值 4.967 4.967 9.933 28.046 — 59.463 — 2.570 —
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表 3 中国服务业碳排放空间网络结构的驱动因素回归结果
Table 3. Regression results of the driving factors regarding spatial network structure of carbon emission from service industry in China
变量 2000年 2006年 2012年 2018年 地理空间邻近性 0.150***
(0.001)0.152***
(0.000)0.162***
(0.000)0.166***
(0.000)服务业碳排放强度差异 −0.054
(0.216)−0.067*
(0.061)−0.149***
(0.001)−0.092*
(0.075)环境规制强度差异 0.078
(0.116)0.058
(0.144)−0.019
(0.335)−0.036*
(0.054)产业结构优化差异 0.536***
(0.000)0.283***
(0.000)0.367***
(0.001)0.320***
(0.000)城镇化水平差异 0.167***
(0.005)0.327***
(0.000)0.371***
(0.000)0.379***
(0.000)投资水平差异 −0.114
(0.124)−0.067
(0.498)−0.060
(0.134)−0.054
(0.129)科技发展水平差异 0.172*
(0.083)0.307***
(0.000)0.275***
(0.000)0.374***
(0.000)截距项 −0.152***
(0.001)−0.109***
(0.000)−0.083***
(0.000)−0.091***
(0.000)R2 0.231 0.284 0.298 0.327 调整后R2 0.226 0.279 0.293 0.322 重复迭代次数 2 000 2 000 2 000 2 000 样本数量 870 870 870 870 注：括号中数值为P值. ***、**和*分别表示在0.01、0.05和0.1水平上显著相关.
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