Spatial and temporal characteristics and the heterogeneity of influencing factors of the synergism of pollution and carbon emissions reduction in Beijing-Tianjin-Hebei urban agglomeration
-
摘要:
基于2003—2020年京津冀城市群数据，利用耦合协调度、空间杜宾和时空地理加权回归模型，探究城市群减污降碳协同效应的时空特征及其影响因素的异质性。结果表明：“十五”“十一五”期间京津冀城市群碳排放量快速增长，进入“十二五”后增速放缓；大气污染物排放在“十二五”达到峰值，“十三五”减排效果显著；研究期内，减污降碳耦合协调水平展现出三阶段波动式上升的特点，分别为缓慢上升、徘徊探索和稳步增长阶段；能源消费强度和能源消费总量是影响城市减污降碳耦合协调水平的直接核心因素，城镇化率、实际人均GDP、产业结构、实际利用外资金额和科学技术支出占比等通过影响能源消费而作用于减污降碳耦合协调水平，且各因素均展现出时空异质性特征。最后，从推动区域减污降碳协同控制、强化重点城市和重点行业差异化协同减排策略、推动能源结构调整、注重绿色技术创新和绿色金融支持等方面提出优化对策。
Abstract:Based on the data of Beijing-Tianjin-Hebei urban agglomeration from 2003 to 2020, the coupling coordination degree, spatial Durbin and spatial-temporal geographical weighted regression models were used to explore the spatial and temporal characteristics of the synergistic effect of pollution reduction and carbon reduction in the urban agglomeration and the heterogeneity of its influencing factors. The study showed that carbon emissions grew rapidly from the 10th to the 11th Five-year Plan periods, and then slowed down after the 12th Five-year Plan period. The emission of atmospheric pollutants reached its peak during the 12th Five-year Plan period, and the emission reduction effect during the 13th Five-year Plan period was significant. During the study period, the level of pollution and carbon reduction coupling and coordination showed three phases of fluctuating increase, namely, slow increase, hovering exploration and steady increase; the energy consumption intensity and total energy consumption were the core factors directly affecting the level of pollution and carbon reduction coupling and coordination in the cities. The urbanization rate, real GDP per capita, industrial structure, the amount of foreign investment utilized and the proportion of science and technology expenditure were the core factors affecting the level of pollution and carbon reduction coupling and coordination by affecting energy consumption, and all factors showed spatial and temporal heterogeneity. Finally, optimized countermeasures were proposed in terms of promoting collaborative control of regional pollution reduction and carbon reduction, strengthening differentiated collaborative emission reduction strategies for key cities and key industries, promoting energy restructuring, and focusing on green technology innovation and green financial support.
-
图 1 2003—2020年京津冀城市群二氧化碳排放和大气污染物排放当量时间演变特征
Figure 1. Temporal evolution of CO2 emission and air pollutant emission equivalents in Beijing-Tianjin-Hebei urban agglomeration in 2003-2020
图 2 2005—2020年京津冀城市群二氧化碳排放和大气污染物排放当量演变特征
Figure 2. Evolution of CO2 emission and air pollutant emission equivalents in Beijing-Tianjin-Hebei urban agglomeration in 2005-2020
图 3 2003—2020年京津冀城市群减污降碳耦合协调度均值变化
Figure 3. Mean value of coupling coordination degree of pollution reduction and carbon reduction in Beijing-Tianjin-Hebei urban agglomeration in 2003-2020
图 4 2003—2020年京津冀三省（市）耦合协调度均值演变情况
Figure 4. Changes in the mean value of coupling coordination degree among Beijing, Tianjin and Hebei in 2003-2020
表 1 协调水平划分标准
Table 1. Criteria for classifying the level of coordination
耦合协调度 耦合协调度（D）取值 协调状态 失调衰退 0<D≤0.1 极度失调 0.1<D≤0.2 严重失调 0.2<D≤0.3 中度失调 过渡调和 0.3<D≤0.4 轻度失调 0.4<D≤0.5 濒临失调 0.5<D≤0.6 勉强协调 低度协调 0.6<D≤0.7 初级协调 0.7<D≤0.8 中级协调 高度协调 0.8<D≤0.9 良好协调 0.9<D≤1.0 优质协调 
下载: 导出CSV
表 2 京津冀城市群二氧化碳与大气污染物协同减排耦合协调度分布
Table 2. Spatial and temporal distribution of coupling coordination degree of carbon dioxide and air pollutant reduction in Beijing-Tianjin-Hebei urban agglomeration
等级分类 2005年 2010年 2015年 2020年 严重失调 中度失调 张家口 承德 轻度失调 衡水 邯郸 濒临失调 秦皇岛、邢台 承德 勉强协调 石家庄、邯郸、承德 秦皇岛、邯郸、衡水、
张家口、天津 承德 初级协调 廊坊 邢台、石家庄 张家口、衡水、邢台 秦皇岛、张家口、衡水 中极协调 保定、北京、沧州、天津 天津、廊坊 石家庄、唐山 天津、 良好协调 唐山 唐山、沧州、保定 廊坊、秦皇岛、北京、沧州、保定 邢台、邯郸 优质协调 北京 廊坊、唐山、保定、沧州、北京、石家庄
下载: 导出CSV
表 3 2003—2020年京津冀城市群OLS回归和SDM回归结果
Table 3. OLS regression and SDM regression results for Beijing-Tianjin-Hebei urban agglomeration in 2003-2020
变量 OLS SDM 固定效应 对本地区影响系数 对其他地区的空间溢出系数 直接效应 间接效应 总效应 能源消费总量 −0.670*** −0.674*** −0.384*** −0.693*** −0.416*** −0.608*** 能源消费强度 −0.297*** −0.352*** −0.267* −0.285*** −0.112* −0.197* 城镇化率 0.004 23** 0.003 71** 0.008 46** 0.003 39** 0.001 59 0.004 99 实际人均GDP 0.166* 0.119* 0.343* 0.119** 0.107* 0.218* 第二产业占比 −0.402*** −0.358** −0.436*** −0.466*** −0.485* −0.019** 外资情况 −0.256** −0.278* −0.447** −0.0114* −0.238 −0.207 科学技术支出占比 0.002 62** 0.006 28*** 0.001 80*** 0.007 49*** 0.005 94** 0.001 17*** 可决系数 0.77 0.83 注：* 、** 、***分别表示在0.1、0.05、0.01水平差异显著。
下载: 导出CSV
表 4 2003年、2020年各因素回归系数空间分布
Table 4. Spatial distribution of regression coefficients for each factor for 2003 and 2020
2003年 2020年 取值范围 城市分布 取值范围 城市分布 能源消费总量 −1.781 800~−0.274 726 北京、唐山、天津 −2.708 550~−0.975 563 唐山、天津、北京、石家庄、邯郸 −0.274 727~1.426 740 邯郸、保定、石家庄、邢台、秦皇岛 −0.975 562~−0.207 805 沧州、邢台、张家口、承德 1.426 741~4.044 812 张家口、廊坊、沧州、衡水、承德 −0.207 804~−0.999 554 廊坊、秦皇岛、保定、衡水 能源消费强度 −2.374 391~−1.466 487 邯郸、唐山、张家口、邢台 −2.011 660~−1.020 530 邢台、邯郸、唐山 −1.466 486~−0.569 701 承德、秦皇岛、石家庄、保定、衡水 −1.020 529~−0.192 500 张家口、承德、天津、衡水 −0.569 700~−0.112 451 廊坊、沧州、北京、天津 −0.102 499~0.169 170 石家庄、秦皇岛、衡水、北京、保定 城镇化率 −6.986 920~−2.085 956 承德、张家口、石家庄、沧州 −3.027 150~−0.996 631 邯郸、承德、沧州、 −2.085 955~0.141 036 保定、廊坊、天津、唐山、北京、
衡水、邯郸−0.996 630~0.659 473 张家口、石家庄、邢台、保定、
衡水、天津0.141 037~3.231 400 邢台、秦皇岛 −0.659 474~5.102 400 廊坊、唐山、秦皇岛、北京 实际人均GDP −8.229 750~−3.169 780 衡水、保定、廊坊、邢台、承德 −1.491 400~−0.744 785 邢台、邯郸、沧州、承德、张家口、 −3.169 779~−0.694 900 北京、唐山、天津、邯郸、秦皇岛 −0.744 784~−0.013 837 衡水、保定、秦皇岛、北京、 − 0.694 901~11.840 301 石家庄、张家口 −0.013 836~3.834 780 石家庄、天津、廊坊、唐山 第二产业
占比−2.384 650~−0.043 562 衡水、石家庄、保定、北京 −2.775 149~−1.092 540 天津、唐山、邢台、邯郸、沧州、 −0.043 561~0.319 277 邯郸、沧州、承德、邢台、秦皇岛 −1.092 541~0.172 604 石家庄、衡水、保定、承德、廊坊 0.319 278~0.634 184 唐山、张家口、廊坊、天津 0.172 605~0.431 869 北京、秦皇岛、张家口 实际利用外资金额 −8.749 236~−4.379 943 张家口、承德、石家庄、邢台、
邯郸、廊坊、−2.371 890~−0.992 030 承德、唐山、张家口、衡水、石家庄 −4.379 942~2.250 720 唐山、沧州、保定 −0.992 029~2.212 920 沧州、邢台、廊坊、 2.250 721~9.380 606 衡水、秦皇岛、天津、北京、 2.212 921~6.693 900 邯郸、秦皇岛、北京、天津、保定、 科学技术支出占比 −2.468 140~−1.814 368 承德、石家庄、张家口、秦皇岛、邯郸 −1.894 220~−1.218 880 沧州、北京 −1.814 367~0.059 273 保定、邢台、北京、廊坊、唐山 −1.218 879~1.024 700 保定、石家庄、邢台、廊坊、衡水、
张家口、天津0.059 274~2.190 724 沧州、衡水 1.024 701~5.564 640 承德、秦皇岛、唐山
下载: 导出CSV
-
[1] 李慧, 王淑兰, 张文杰, 等.京津冀及周边地区 “2+26” 城市空气质量特征及其影响因素[J]. 环境科学研究,2021,34(1):172-184.LI H, WANG S L, ZHANG W J, et al. Characteristics and influencing factors of urban air quality in Beijing-Tianjin-Hebei and its surrounding areas ("2+26"cities)[J]. Research of Environmental Sciences,2021,34(1):172-184. [2] 李建呈, 王洛忠.区域大气污染联防联控的政策效果评估: 基于京津冀及周边地区 “2+26” 城市的准自然实验[J]. 中国行政管理,2023(1):75-83.LI J C, WANG L Z. Quantitative assessment of the effects of joint prevention and control of regional air pollution: a quasi-natural experiment based on "2+26" cities[J]. Chinese Public Administration,2023(1):75-83. [3] 李云燕, 盛清, 代建.基于DMSP-OLS与NPP-VIIRS整合数据的京津冀城市群碳排放时空演变特征[J]. 环境工程技术学报,2023,13(2):447-454.LI Y Y, SHENG Q, DAI J. Spatio-temporal evolution characteristics of carbon emissions in Beijing-Tianjin-Hebei urban agglomeration derived from integrated data of DMSP-OLS and NPP-VIIRS[J]. Journal of Environmental Engineering Technology,2023,13(2):447-454. [4] 韩楠, 罗新宇.多情景视角下京津冀碳排放达峰预测与减排潜力[J]. 自然资源学报,2022,37(5):1277-1288. doi: 10.31497/zrzyxb.20220512HAN N, LUO X Y. Carbon emission peak prediction and reduction potential in Beijing-Tianjin-Hebei region from the perspective of multiple scenarios[J]. Journal of Natural Resources,2022,37(5):1277-1288. doi: 10.31497/zrzyxb.20220512 [5] 俞珊, 张双, 张增杰, 等.北京市 “十四五” 时期大气污染物与温室气体协同控制效果评估研究[J]. 环境科学学报,2022,42(6):499-508.YU S, ZHANG S, ZHANG Z J, et al. Assessment of co-control effects for air pollutants and greenhouse gases in Beijing during the 14th Five-Year Plan period[J]. Acta Scientiae Circumstantiae,2022,42(6):499-508. [6] 刘茂辉, 岳亚云, 刘胜楠, 等.基于STIRPAT模型天津减污降碳协同效应多维度分析[J]. 环境科学,2023,44(3):1277-1286. doi: 10.13227/j.hjkx.202204086LIU M H, YUE Y Y, LIU S N, et al. Multi-dimensional analysis of the synergistic effect of pollution reduction and carbon reduction in Tianjin based on the STIRPAT model[J]. Environmental Science,2023,44(3):1277-1286. doi: 10.13227/j.hjkx.202204086 [7] 杨添棋, 王洪昌, 张辰, 等.京津冀及周边地区 “2+26” 城市结构性调整政策的CO2协同减排效益评估[J]. 环境科学,2022,43(11):5315-5325.YANG T Q, WANG H C, ZHANG C, et al. Carbon dioxide mitigation co-effect analysis of structural adjustment measures in the "2+26" cities in the Jing-Jin-ji region and its surroundings[J]. Environmental Science,2022,43(11):5315-5325. [8] 吴华成, 周卫青, 李朋, 等.京津冀与关中地区“以电代煤”大气环境与碳减排效益评估[J]. 全球能源互联网,2022(2):166-172. doi: 10.19705/j.cnki.issn2096-5125.2022.02.008WU H C, ZHOU W Q, LI P, et al. Atmospheric environment and carbon emission reduction benefit evaluation of "coal-to-electricity" project in Beijing-Tianjin-Hebei and Guanzhong region[J]. Journal of Global Energy Interconnection,2022(2):166-172. doi: 10.19705/j.cnki.issn2096-5125.2022.02.008 [9] LIN C Q, LOUIE P K K, LAU A K H, et al. Net effect of air pollution controls on health risk in the Beijing-Tianjin-Hebei region during the 2022 winter Olympics and Paralympics[J]. Journal of Environmental Sciences,2024,135:560-569. doi: 10.1016/j.jes.2022.10.008 [10] 王文燕, 冯翰林, 郭二民.减污降碳协同治理纳入生态环境管理体系探讨[J]. 环境工程技术学报,2022,12(6):1882-1889.WANG W Y, FENG H L, GUO E M. Discussion on the integration of pollution reduction and carbon reduction collaborative governance into the ecological environment management system[J]. Journal of Environmental Engineering Technology,2022,12(6):1882-1889. [11] 戴静怡, 曹媛, 陈操操.城市减污降碳协同增效内涵、潜力与路径[J]. 中国环境管理,2023,15(2):30-37.DAI J Y, CAO Y, CHEN C C. Synergistic connotations, potential and paths of urban pollution and carbon emissions reduction[J]. Chinese Journal of Environmental Management,2023,15(2):30-37. [12] 毛显强, 曾桉, 邢有凯, 等.从理念到行动: 温室气体与局地污染物减排的协同效益与协同控制研究综述[J]. 气候变化研究进展,2021,17(3):255-267.MAO X Q, ZENG A, XING Y K, et al. From concept to action: a review of research on co-benefits and co-control of greenhouse gases and local air pollutants reductions[J]. Climate Change Research,2021,17(3):255-267. [13] 高庆先, 高文欧, 马占云, 等.大气污染物与温室气体减排协同效应评估方法及应用[J]. 气候变化研究进展,2021,17(3):268-278.GAO Q X, GAO W O, MA Z Y, et al. The synergy effect assessment method and its application for air pollutants and greenhouse gases reduction[J]. Climate Change Research,2021,17(3):268-278. [14] 易文杰,李庄,罗竹燕等.水泥行业环境影响评价低碳技术选择与应用[J]. 环境工程技术学报,2022,12(6):1905-1914. doi: 10.12153/j.issn.1674-991X.20220590YI W J,LI Z,LUO Z Y,et al. Selection and application of low carbon technologies in environmental impact assessment of cement industry[J]. Journal of Environmental Engineering Technology,2022,12(6):1905-1914. doi: 10.12153/j.issn.1674-991X.20220590 [15] 朱思瑜, 于冰.“排污权”和“碳排放权”交易的减污降碳协同效应研究: 基于污染治理和政策管理的双重视角[J]. 中国环境管理,2023,15(1):102-109.ZHU S Y, YU B. Research on the co-benefits of pollution reduction and carbon reduction of "emissions trading" and "carbon emissions trading": based on the dual perspectives of pollution control and policy management[J]. Chinese Journal of Environmental Management,2023,15(1):102-109. [16] 田嘉莉, 付书科, 刘萧玮.财政支出政策能实现减污降碳协同效应吗[J]. 财政科学,2022(2):100-115. doi: 10.3969/j.issn.2096-1391.2022.02.008TIAN J L, FU S K, LIU X W. Can fiscal expenditure policy achieve synergistic effects of pollution reduction and carbon reduction[J]. Fiscal Science,2022(2):100-115. doi: 10.3969/j.issn.2096-1391.2022.02.008 [17] 张为师, 徐颖, 惠婧璇.中国城市CO2排放和空气质量协同变化特征及驱动因素研究[J]. 中国环境管理,2023,15(2):38-47.ZHANG W S, XU Y, HUI J X. The spatio-temporal impacts and driving factors of the synergistic effects of reducing pollution and carbon emissions in cities of China[J]. Chinese Journal of Environmental Management,2023,15(2):38-47. [18] 陈晓红, 张嘉敏, 唐湘博.中国工业减污降碳协同效应及其影响机制[J]. 资源科学,2022,44(12):2387-2398.CHEN X H, ZHANG J M, TANG X B. Synergistic effect of industrial air pollution and carbon emission reduction in China and influencing mechanism[J]. Resources Science,2022,44(12):2387-2398. [19] 唐湘博, 张野, 曹利珍, 等.中国减污降碳协同效应的时空特征及其影响机制分析[J]. 环境科学研究,2022,35(10):2252-2263.TANG X B, ZHANG Y, CAO L Z, et al. Spatio-temporal characteristics and influencing mechanism of synergistic effect of pollution and carbon emission reduction in China[J]. Research of Environmental Sciences,2022,35(10):2252-2263. [20] 狄乾斌, 陈小龙, 侯智文.“双碳” 目标下中国三大城市群减污降碳协同治理区域差异及关键路径识别[J]. 资源科学,2022,44(6):1155-1167.DI Q B, CHEN X L, HOU Z W. Regional differences and key pathway identification of the coordinated governance of pollution control and carbon emission reduction in the three major urban agglomerations of China under the "double-carbon" targets[J]. Resources Science,2022,44(6):1155-1167. [21] 张瑜, 孙倩, 薛进军, 等.减污降碳的协同效应分析及其路径探究[J]. 中国人口·资源与环境,2022,32(5):1-13.ZHANG Y, SUN Q, XUE J J, et al. Synergistic effects of pollution control and carbon reduction and their pathways[J]. China Population, Resources and Environment,2022,32(5):1-13. [22] 王淑佳, 孔伟, 任亮, 等.国内耦合协调度模型的误区及修正[J]. 自然资源学报,2021,36(3):793-810. doi: 10.31497/zrzyxb.20210319WANG S J, KONG W, REN L, Et al. Research on misuses and modification of coupling coordination degree model in China[J]. Journal of Natural Resources,2021,36(3):793-810. doi: 10.31497/zrzyxb.20210319 [23] 吴建新, 郭智勇.基于连续性动态分布方法的中国碳排放收敛分析[J]. 统计研究,2016,33(1):54-60. doi: 10.19343/j.cnki.11-1302/c.2016.01.008WU J X, GUO Z Y. Research on the convergence of carbon dioxide emissions in China: a continuous dynamic distribution approach[J]. Statistical Research,2016,33(1):54-60. ⊕ doi: 10.19343/j.cnki.11-1302/c.2016.01.008 -
下载:
点击查看大图
计量
- 文章访问数: 515
- HTML全文浏览量: 129
- PDF下载量: 99
- 被引次数: 0
