中国城市空气质量时空特征及其影响因素的地理探测（英文）

Ambient air pollution brought by the rapid economic development and industrial production in China has exerted a significant influence on socio-economic activities and public health, especially in the densely populated urban areas. Therefore, scientific examination of regional variation of urban air quality and its dominant factors is of great importance to regional environmental management. Based on daily air quality index(AQI) datasets spanning from 2014 to 2016, this study analysed the spatiotemporal characteristics of air quality across different regions throughout China and ascertained the determinants of urban air quality in disparate regions. The main findings are as follows:(1) The annual average value of the urban AQI in China decreased from 2014 to 2016, indicating a desirable trend in air quality at the national scale.(2) The attainment rate of the urban AQI exhibited an apparent spatially stratified heterogeneity, wherein North China retained a high AQI value. The increase of Moran's I Index reported an apparent spillover effect among adjacent regions.(3) Both at the national and regional scales, the seasonal tendency of air quality in each year is similar, wherein good in summer and relatively poor in winter.(4) Results drawn from the Geographic Detector analysis show that dominant factors influencing AQI vary significantly across urban agglomerations. Topographical and meteorological variations in urban areas may lead to complex spatiotemporal variations in pollutant concentration. Whereas given the same natural conditions, the human-dominated factors, such as industrial structure and urban form, exert significant impacts on urban air quality.The spatial spillover effects and regional heterogeneity of urban air quality illustrated in this study suggest the governments and institutions should set priority to the importance of regional cooperation and collaboration in light of environment regulation and pollution prevention.     

中国城市空气质量时空演化特征及社会经济驱动力

城市空气污染是中国在快速城镇化和经济发展过程中亟待解决的难题。利用2013年和2014年全国城市空气质量数据,综合ArcGIS空间分析和统计分析,从年度、季节、月份、小时4个时间尺度比较归纳了全国城市空气质量的时空间演化特征,并采用空间计量模型,从全国和区域两个空间尺度,量化分析了城市空气质量变化的社会经济驱动力。结果表明：① 全国城市空气质量全年达标天数比例增加,但空气污染程度加重,高污染区域恶化态势明显;② 城市空气质量与生产生活活动表现出一定的时间耦合性,基本呈现“日出趋差、日落趋优”的态势;③ 全国城市空气污染表现出“东重西轻、北重南轻”的空间格局,区域一体化态势明显;④ 区域城市空气污染的总体程度和分布结构具有明显的分异特征,区域空气污染形成和演化路径可基本归纳为：“重点城市污染加重—重点城市污染扩散—区域整体污染加重—重点城市引领治污—区域污染联防联控—区域整体污染减轻”;⑤ 从全国层面看,能源消耗、工业化和技术进步是推动城市空气质量恶化的重要因素,经济发展对城市空气质量改善具有显著的推动作用。⑥ 受各地区资源环境基础和社会经济发展阶段影响,各类社会经济因素对城市空气质量改善的驱动方向和驱动强度差异明显。在结论的基础上,讨论了中国经济发展和环境变化关系的区域分异以及发展理念。     

Spatiotemporal evolution of urban air quality and socioeconomic driving forces in China

Air pollution is a serious problem brought by the rapid urbanization and economic development in China, imposing great challenges and threats to population health and the sustainability of the society. Based on the real-time air quality monitoring data obtained for each Chinese city from 2013 to 2014, the spatiotemporal characteristics of air pollution are analyzed using various exploratory spatial data analysis tools. With spatial econometric models, this paper further quantifies the influences of socioeconomic factors on air quality at both the national and regional scales. The results are as follows: (1) From 2013 to 2014, the percentage of days compliance of urban air quality increased but air pollution deteriorated and the worsening situation in regions with poor air quality became more obvious. (2) Changes of air quality show a clear temporal coupling with regional socioeconomic activities, basically “relatively poor at daytime and relatively good at night”. (3) Urban air pollution shows a spatial pattern of “heavy in the east and light in the west, and heavy in the north and light in the south”. (4) The overall extent and distribution of regional urban air pollution have clearly different characteristics. The formation and evolution of regional air pollution can be basically induced as “the pollution of key cities is aggravated—pollution of those cities spreads—regional overall pollution is aggravated—the key cities lead in pollution governance—regional pollution joint prevention and control is implemented—regional overall pollution is reduced”. (5) At the national level, energy consumption, industrialization and technological progress are the major factors in the worsening of urban air quality, economic development is a significant driver for the improvement of that quality. (6) Influenced by resources, environment and the development stage, the socioeconomic factors had strongly variable impacts on air quality, in both direction and intensity in different regions. Based on the conclusion, the regional differentiation and development idea of the relationship between economic development and environmental changes in China are discussed.     

基于空间计量模型的中国城市化发展与城市空气质量关系

采用中国289个地级及以上城市2016年的空气质量指数（AQI）和夜间灯光数据,运用空间滞后模型,从空间溢出效应视角出发探究了城市化发展与空气质量之间的定量关系。从空间滞后模型的估计结果来看,城市空气污染存在显著的空间溢出效应,周边地区空气质量的下降会导致本地空气污染情况加重。在城市发展过程中,城市经济发展水平的提高,引致的城市建设用地的扩张使得空气质量不断恶化;政府管制力的加强对空气污染的治理起到了促进作用,而居民环保意识和城市技术创新水平的提高有利于空气质量的改善。此外,PM2.5质量浓度的上升导致空气质量恶化。公路货运量在统计上与空气质量的关系不显著。从研究结果来看,协调城市发展与空气质量的关系以及加强空气污染防治的联防联控机制,是未来空气污染治理工作的重点。     

中国城市环境污染监管水平的空间演化特征与影响因素

基于中国2010—2016年的地级市污染监管水平数据,采用空间自相关、位序-规模法则,空间计量模型等地学方法,分析中国地级城市污染监管水平的时空分异特征和影响因素及其空间溢出效应。结果表明：① 2010—2016年中国污染监管水平整体上呈现上升趋势且区域性和集聚性特征明显,东南部地区为稳定的高值集聚地区,中西部地区为稳定的低值集聚地区。② 中国城市污染监管水平属于次位型分布,监管规模分布的分散趋势大于集中趋势。③ 城市的人口密度、经济发展水平、第二产业占比等对城市污染监管水平有显著的正向影响,城市规模对污染监管水平存在负向的影响。④ 城市经济发展水平和城市人口密度对污染监管水平起到显著的直接效应;第二产业占比、二氧化硫排放量等起到了显著的间接效应。     

长三角地区产业转型的大气污染效应——基于空间溢出视角

The Yangtze River Delta(YRD) is a region in China with a serious contradiction between economic growth and environmental pollution. Exploring the spatiotemporal effects and influencing factors of air pollution in the region is highly important for formulating policies to promote the high-quality development of urban industries. This study uses the spatial Durbin model(SDM) to analyze the local direct and spatial spillover effects of industrial transformation on air pollution and quantifies the contribution of each factor. From 2008 to 2018, there was a significant spatial agglomeration of industrial sulfur dioxide emissions(ISDE) in the YRD, and every 1% increase in ISDE led to a synchronous increase of 0.603% in the ISDE in adjacent cities. The industrial scale index(ISCI) and industrial structure index(ISTI), as the core factors of industrial transformation, significantly affect the emissions of sulfur dioxide in the YRD, and the elastic coefficients are 0.677 and-0.368, respectively. The order of the direct effect of the explanatory variables on local ISDE is ISCI>ISTI>foreign direct investment(FDI)>enterprise technological innovation(ETI)>environmental regulation(ER)> per capita GDP(PGDP). Similarly, the order of the spatial spillover effect of all variables on ISDE in adjacent cities is ISCI>PGDP>FDI>ETI>ISTI>ER, and the coefficients of the ISCI and ISTI are 1.531 and 0.113, respectively. This study contributes to the existing research that verifies the environmental Kuznets curve in the YRD, denies the pollution heaven hypothesis, indicates the Porter hypothesis, and provides empirical evidence for the formation mechanism of regional environmental pollution from a spatial spillover perspective.     

Assessment of Urban Air Pollution and Spatial Spillover Effects in China: Cases of 113 Key Environmental Protection Cities

With rapid urbanization and energy consumption, environmental pollution and degradation have become increasingly serious problems in China. At the beginning of 2013, China implemented new ambient air quality standards (GB 3095-2012) in which the concentration of six pollutants including PM_2.5, ozone, carbon monoxide, PM₁₀, sulfur dioxide and nitrogen dioxide were monitored. This study gathered annual air pollutant concentration data for the six pollutants in 113 key environmental protection cites throughout China in 2014 and 2015 to explain spatial patterns of urban air pollution. Based on the Kernel density estimation method, spatial hotspots of air pollution were illustrated through which spatial cluster of each pollutants could be plotted. By employing an entropy evaluation system, urban air quality was assessed in terms of the six atmospheric pollutants. We conclude that, in general, CO and SO₂ were two important pollutants in most Chinese cities, but this varied greatly among cities. The assessment results indicate that cities with the worst air quality were mainly located in northern and central provinces, dominantly in the Beijing-Tianjin-Hebei metropolitan area. Regression modeling showed that a combination of meteorological factors and human-related determinants, to say specifically, industrialization and urbanization factors, greatly influenced urban air quality variation in China. Results from spatial lag regression modeling confirmed that air pollution existed obvious spatial spillover effects among key cities. The spatial interdependence effects of urban air quality means that Chinese municipal governments should strengthen regional cooperation and deepen bilateral collaboration in terms of air regulation and pollution prevention.     

外商直接投资对空气污染影响的空间异质性分析——以中国150个城市空气质量指数（AQI）为例

空气污染问题引起了人们极大的关注。以中国2014年150个地级市作为样本数据,采用空气质量指数（AQI）作为全面衡量空气污染的指标,运用地理加权回归模型从空间异质性视角出发,分析了不同城市外商直接投资与空气污染之间的关系。研究结果表明：总体上,外商直接投资由于带来了先进的技术,有利于空气质量的改善。此外,人均地区生产总值的增加、二氧化硫和PM_2.5浓度的提高均是导致空气污染加剧的重要因素;而环保意识的提升则有利于空气质量的改善。民用汽车保有量变量在统计上不显著。从地理加权回归模型估计结果来看,不同城市的外商直接投资对环境的改善作用存在显著的空间异质性。其中,东北城市群、关中城市群和长江中游城市群外商直接投资对空气质量的改善作用最大,山东半岛城市群和川渝城市群外商直接投资对空气质量的改善作用不明显。     

空气污染对城市活力的影响及其建成环境异质性——基于大数据的分析

建设充满活力的城市空间得到地理和城乡规划学者的广泛关注。随着空气污染问题的加剧,空气质量影响居民在城市空间中的活动,但鲜有研究考察空气污染与城市活力的定量关系。基于广州市2019年新浪微博签到记录、日气象和空气质量数据、以及建成环境数据,本研究构建以街道为空间单元、以天为时间单元的面板数据,通过标准差椭圆（SDE）以及面板回归模型测度空气污染对城市活力的抑制效应以及该抑制效应在不同建成环境上的异质性。研究得到以下结论：① 城市活力SDE面积随空气质量指数（AQI）上升而收缩,轻度污染和中度污染的城市活力SDE面积仅为空气质量优的约80%和30%。② 运用空间面板回归模型控制街道的空间关联性后,空气质量指数（AQI）对城市活力具有明显负向影响,AQI每增加1个单位,日活动强度减少约0.10次/10 km²;当空气质量恶化到中等污染后,AQI每增加1个单位,日活动强度减少约0.14次/10 km²。③ 空气污染对城市活力的抑制效应在不同建成环境上存在异质性,POI密度、离城市中心距离强化空气污染对城市活力的抑制效应,而地铁站密度、道路交叉口密度、土地利用混合度则弱化空气污染对城市活力的抑制效应。本研究有助于更好厘清空气污染、建成环境与城市活力的关系,并为优化建成环境以缓减空气污染对城市活力抑制效应提供分析支撑。     

Spatio-temporal Differences and Driving Forces of Air Quality in Chinese Cities

Deterioration of city air quality is the serious problem in the process of rapid urbanization and economic development in China. Taking 74 cities that have implemented the new Ambient Air Quality Standards (GB3095- 2012) since 2013 as examples, using statistical and ArcGIS spatial analysis method, the multi-scale temporal and spatial variations characteristics and the impact of social and economic factors on urban air quality variations, are analyzed in this paper. The main research conclusions are as follows:(1) Air quality in Chinese cities shows very significant seasonal variations, with higher air quality in summer and autumn, and lower air quality in spring and winter; (2) Seen from a daily pollution perspective, air pollution is very serious and will tend to be more serious in the future; (3) Seen from an hourly variation perspective, urban air quality is time coupled with social production and urban living; (4) The overall spatial pattern of urban air quality is high in the east and north and low in the west and south, but with an obvious trend towards regional integration. (5) Cities in different regions have different factors that cause air quality variations. In general, urbanization level and energy consumption per GDP are the common factors.     

长江经济带空气质量指数的时空特征及驱动因素分析 ——基于贝叶斯空间计量模型的实证

基于2015年长江经济带126个城市空气质量监测数据,首先利用探索性空间数据分析方法揭示了空气质量指数（AQI）的时空演变特征,然后采用贝叶斯空间滞后模型探讨了长江经济带空气质量指数的社会经济驱动因素。研究结果表明：① 长江经济带年AQI在空间上整体具有东高西低,长江以北高长江以南低的分布特点,具有明显的空间集聚特征。空气污染严重的热点地区主要集中长三角城市群的江苏省、浙北地区、皖北大部分地区以及上海市。空气质量较好的冷点地区则主要集中在云南省、四川的攀枝花以及贵州的大部分地区。② 长江经济带AQI在季节上呈现冬春高、夏秋低的季节变化趋势。总体而言,四季的高值集聚主要分布在鄂皖苏,低值集聚主要分布在云贵地区。③ 贝叶斯空间滞后模型回归结果显示,长江经济带空气质量存在显著的空间溢出效应。此外,模型结果证实了“环境库兹涅兹曲线”假说;FDI回归系数为正,支持了“污染避难所”假说;人口密度、公路客运量均是导致空气污染加剧的重要因素,而第三产业比重和建成区绿化覆盖率增加有利于长江经济带空气质量的改善。     

The impact of energy-intensive industries on air quality in China’s industrial agglomerations

Understanding the driving forces of regional air pollution and its mechanism has gained much attention in academic research,which can provide scientific policy-making basis for economy-environment sustainability in China.Being an important energy and industrial base,the North China Plain region has been experiencing severe air pollution.Therefore,understanding the relationship between industrialization and air quality in this region is of great importance for air quality improvement.In this study,the average annual concentrations of SO2,NO2 and PM10 in 47 sample cities at and above the prefecture level in the North China Plain region from 2007 to 2016 were used to illustrate the spatiotemporal characteristics of air pollution within this region.Furthermore,panel data model,panel vector autoregression model,and impulse response function were used to explore the correlation and driving mechanism between energy-intensive industries and regional air quality.The results show that:first,overall air quality improved in the study area between 2007 and 2016,with a significant greater fall in concentration of SO2 than that of NO2 and PM10;second,provincial border areas suffered from severe air pollution and showed an apparent spatial agglomeration trend of pollution;and third,the test results from different models all proved that energy-intensive industries such as the chemical,non-metallic mineral production,electric and thermal power production and supply industries,had a significant positive correlation with concentrations of air pollutants,and indicated an obvious short-term impulse response effect.It concludes that upgradation of industrial structure,especially that of energy-intensive industries,plays a very important role in the improvement of regional air quality,which is recommended to be put in top priority for authorities.Therefore,policies as increasing investments in technological innovation in energy-intensive industries,deepening cooperation in environmental governance between different provinces and cities,and strengthening supervision and entry restrictions are suggested.     

中国城市空气质量的区域差异及归因分析

开展城市空气质量时空格局演变特征及其影响因素的研究,对于深入认识城市环境与社会经济系统的互馈机理、制定高效的环境治理措施、提升城市发展质量具有重要的理论与实践意义。本文以中国全面实施《大气污染防治行动计划》的2014年为起点,刻画了2014—2019年286个地级以上城市6种空气污染物浓度（CO、NO₂、O₃、PM₁₀、PM_2.5、SO₂）的时空演变特征,并基于面板回归模型分析各污染物浓度之间的相互作用关系;进而利用随机森林模型对城市6种空气污染物浓度与13个自然和社会经济影响因子的关联强度进行探究,从中梳理出关键影响因子。结果显示：① 研究期内,O₃污染加剧,其余污染物年均浓度逐年下降,其中SO₂浓度降幅最大。虽然典型的重污染区范围有所减小,但京津冀、山东半岛、山西、河南等地区的城市空气污染物浓度仍相对较高。② 城市6种空气污染物浓度之间存在显著的相互影响关系,城市空气复合污染特征明显。③ 自然因素和社会经济因素对不同种类空气污染物浓度的影响差异较大,且与污染物浓度之间呈非线性响应关系。自然因素中,城市年均气温与空气污染物浓度的关联强度最大,其次是植被指数。社会经济影响因素中,土地城市化水平和二产比重是主导影响因子,其次是电力消耗总量和交通因子。偏依赖分析进一步刻画了不同污染物浓度对主导影响因子的响应突变阈值。鉴于人类对于自然环境和气象条件的控制能力有限,建议继续通过优化城市密度、控制人为排放源及严格的空气污染防控措施以进一步有效提高城市空气质量。     

From highly polluted inland city of China to “Lanzhou Blue”: The air-pollution characteristics

Air quality was improved considerably and the so-called "Lanzhou Blue" appeared frequently in Lanzhou due to implementation of some strict emission-control measures in recent years. To better understand whether the concentration of each air pollutant had decreased significantly and then give some suggestions as to urban air-quality improvement in the near future, the variations of the Air Quality Index (AQI) and six criterion air pollutants (PM_2.5, PM₁₀, CO, SO₂, NO₂, and O₃) at five state-controlled monitoring sites of Lanzhou were studied from 2013 to 2016. The AQI, PM_2.5, PM₁₀, and SO₂ gradually decreased from 2013 to 2016, while CO and NO₂ concentrations had slightly increasing trends, especially in urban areas, due to the large number of motor vehicles, which had an annual growth rate of 30.87%. The variations of the air pollutants in the no-domestic-heating season were more significant than those in the domestic-heating season. The increase of ozone concentration for the domestic-heating season at a background station was the most significant among the five monitoring sites. The vehicle-exhaust and ozone pollution was increasingly severe with the rapid increase in the number of motor vehicles. The particulate-matter pollution became slight in the formerly highly polluted Lanzhou City. Some synergetic measures in urban and rural areas of Lanzhou should be taken by the local government in the near future to control fine particulate-matter (PM_2.5) and ozone pollution.     

中国城市空气污染时空分布格局和人口暴露风险

近年来,中国空气污染及其对人口健康危害的时空分布呈现出新的特征。论文使用5 a(2015—2019年)间逐小时的空气质量监测数据,利用变化率计算、热点分析、趋势分析和超标频数统计等方法,分析了中国332个城市的空气质量及人口空气污染暴露风险的时空分布特征,结论如下：① 中国城市近年来空气质量有好转趋势,环境空气质量指数(AQI)下降的城市占所研究城市总数的91.3%;PM_2.5、PM₁₀、SO₂和CO等4种污染物浓度均有所下降,而NO₂和O₃浓度有所上升;② PM_2.5、PM₁₀、SO₂和CO浓度变化率的热点分布于新疆地区和云南—华南地区,NO₂浓度变化率的热点为新疆地区和河套平原,O₃浓度变化率的热点为华北平原至长江中下游流域;西北地区和华南地区空气质量变化幅度较小;③ 9个城市在PM_2.5、PM₁₀、SO₂、NO₂、O₃和CO等6种污染物中均有暴露,分布于山西、河北与山东。暴露风险均为0级的低风险城市共有12个,分别位于新疆、云南、贵州、四川、广东、福建和黑龙江。研究结论对于跨区域空气污染的协同治理以及制定差异化的空间人口流动管理政策具有重要参考价值。     

航空和高铁对中国城市创新能力的影响

交通运输是创新网络中人才流、资本流等知识与技术流动的物理空间承载,其对城市的创新能力影响已成为经济地理学的交叉前沿热点。论文基于2007—2018年中国城市尺度数据,以航空和高铁运输为例,构建交通运输对城市创新能力影响效应的理论框架,采用双向固定效应面板回归模型,实证检验航空和高铁对城市创新能力的多重异质性机制,并探讨了知识传播、资本积累、产业升级在交通运输与创新能力之间的中介效应。研究发现：① 航空和高铁建设均对城市创新能力有显著正向影响,高铁对城市的创新溢出效应约为航空的3倍。② 航空和高铁对不同类型城市的创新溢出效应存在显著异质性。城市等级异质性方面,航空和高铁对中心城市创新能力的正向影响强度高于非中心城市。人口规模异质性方面,航空对大、中城市创新能力提升有显著正向影响,对小城市有抑制作用;高铁运输则对不同人口规模城市的创新能力均有正向影响,呈现大城市>中等城市>小城市的态势。区域异质性方面,航空和高铁对东、中、西、东北地区的创新能力均有不同程度的提升作用,表现出显著的“马太效应”,东部地区优势地位凸显。③ 航空和高铁均可通过促进技术转移、风险资本配置、外商资本配置间接促使城市创新能力提升。此外,航空还能够通过促进产业升级间接促使城市创新能力提升。     

中国空气质量时空变化特征

基于2015年中国343个地区空气质量指数（AQI）数据,运用统计分析和空间自相关分析方法,对全国及陆地表层系统九大区域空气质量时空变化特征进行分析。结果表明：① 全国及九大区域空气质量春季较差、夏秋优良、冬季最差,AQI季节均值呈现“U”形变化特征。② 全 国及九大区域AQI月均值变化趋势呈现“L”形特征。③ 全国及九大区域AQI日均值变化趋势呈现周期性W~脉冲型波动规律。④ “高污染”和“低污染”区域呈现出北高南低的南北分异格局。⑤ 九大区域首要污染物频次结构差异明显,频次最高的首要污染物PM_2.5或者PM₁₀在空间上呈现出明显的东西分异格局。     

中国366个城市空气污染综合程度的时空演变特征分析

采用2016—2017年中国366个城市1 484个监测站点的空气质量近地监测数据,以期最大程度覆盖西部地区,填补现状对西北热点地区污染因子探讨的空白。运用空间描述性统计、空间插值、空间自相关、标准差椭圆与重心分析方法,从年度、季度、月度、日度对比的角度探讨中国空气质量的污染类型、发展趋势、集聚与迁移特征。研究发现:基于插值法,可将全国分为3个区域,临海性与山地性对空气质量优良区产生显著的正响应。山西是污染加重程度最大的省份,主要受SO₂浓度升高影响,而北京与河南是污染改善效果最明显的省份,主要是PM_2.5的治理成效显著。空气质量分布格局以新疆西部和冀鲁豫形成双核高值聚类模式,并且集聚深受温度分带的影响。空气质量总体分布朝向呈东北—西南方向,转移重心均分布在河南省,以向东北方向移动为主,这种分布变迁又重新定义了其季节分异。全国主要的污染类型以PM₁₀与PM_2.5为主。PM10主要分布在新疆地区,受风沙过境扬尘污染自然因素更大,PM_2.5主要分布在华中、华北、苏北地区,受人为经济活动影响更大。     

高铁站可达性与城乡居民收入差异空间分异研究

随着中国高速铁路迅速发展,高铁站可达性与城乡居民收入差异的关系逐渐成为学术热点问题。在梳理已有研究基础上,论文首先系统揭示了高铁站可达性影响城乡居民收入差异的理论机制;然后通过构建城乡居民收入差异和高铁站可达性指标,并选取2010—2017年全国地级市数据,基于邻近矩阵构建空间杜宾模型,开展了高铁站可达性对城乡居民收入差异空间分异影响研究。结果表明：① 全国层面,高铁站可达性显著正向影响城乡居民收入差异,并且存在显著的空间溢出效应;② 区域层面,高铁站可达性对中西部地区城乡收入差异具有显著正向影响作用,对东部地区城乡居民收入影响则不明显;③ 时间维度,分2010—2014、2010—2015、2010—2016、2010—2017年几个不同时间段来看,高铁站可达性影响城乡居民收入差异的空间溢出效应总体呈现上升趋势,但2010—2017年的空间溢出效应开始减弱。优化新增高铁站区域布局、统筹城乡交通基础设施建设是实现城乡居民收入差异缩小的重要途径。     

Driving factors and spatiotemporal effects of environmental stress in urban agglomeration: Evidence from the Beijing–Tianjin–Hebei region of China

Environmental stress is used as an indicator of the overall pressure on regional environmental systems caused by the output of various pollutants as a result of human activities. Based on the pollutant emissions and socioeconomic databases of the counties in Beijing–Tianjin–Hebei region, this paper comprehensively calculates the environmental stress index(ESI) for the urban agglomeration using the entropy weight method(EWM) at the county scale and analyzes the spatiotemporal patterns and the differences among the four types of major functional zones(MFZ) for the period 2012–2016. In addition, the socioeconomic driving forces of environmental stress are quantitatively estimated using the geographically weighted regression(GWR) method based on the STIRPAT model framework. The results show that:(1) The level of environmental stress in the Beijing–Tianjin–Hebei region was significantly alleviated during that time period, with a decrease in ESI of 54.68% by 2016. This decrease was most significant in Beijing, Tangshan, Tianjin, Shijiazhuang, and other central urban areas, as well as the Binhai New Area. The level of environmental stress in counties decreased gradually from the central urban areas to the suburban areas, and the high-level stress counties were eliminated by 2016.(2) The spatial spillover effect of environmental stress increased further at the county scale from 2012 to 2016, and spatial locking and path dependence emerged in the cities of Tangshan and Tianjin.(3) Urbanized zones(development-optimized and development-prioritized zones) were the major areas bearing environmental pollution in the Beijing–Tianjin–Hebei region in that time period. The ESI accounted for 65.98% of the whole region, where there was a need to focus on the prevention and control of environmental pollution.(4) The driving factors of environmental stress at the county scale included population size and the level of economic development. In addition, the technical capacity of environmental waste disposal, the intensity of agricultural production input, the intensity of territorial development, and the level of urbanization also had a certain degree of influence.(5) There was spatial heterogeneity in the effects of the various driving factors on the level of environmental stress. Thus, it was necessary to adopt differentiated environmental governance and reduction countermeasures in respect of emission sources, according to the intensity and spatiotemporal differences in the driving forces in order to improve the accuracy and adaptability of environmental collaborative control in the Beijing–Tianjin–Hebei region.