近年来，全球气候变化引发的风险事件频发，给人类生存带了巨大威胁。能源消费活动排放的过量二氧化碳是引起气候变化效应的主要诱因，而城市群作为人类活动强度最高的区域，面临着产业能源消费量持续走高和人口数量快速上升引起的生产端和生活端碳排放量增长，以及生态空间受挤压引起的生态空间碳储量下降等问题，以城市群地区作为重点区域开展“生产-生活-生态”（三生）降碳，对缓解全球气候变化有着深远的意义。

作为我国三大城市群之一，京津冀城市群在快速城市化的进程中，区域高质量发展受到过量碳排放的严重掣肘。京津冀城市群定位于发展成为以首都为核心的世界级城市群，生态修复环境改善示范区，“三生”协同降碳以推动城市群碳达峰和碳中和对于美丽中国建设和绿色高质量发展具有重要的示范意义。基于此，本研究从协同学、能源经济学、循环经济学、空间经济学等多个学科理论出发，构建 “三生”协同降碳理论及对应的研究方法体系。以城市群历史碳排放核算和影响因素分析结果为基础，设置减排情景对城市群未来生产端和生活端碳排放进行预测；以城市群生态空间质量评估结果和用地类型转化情况分析为基础，设置碳增储情景对城市群未来生态端碳储量变化进行预测；结合系统协同效应，伺服效应，自组织效应以及空间效应原理，基于碳排放量和碳储量预测结果计算“三生”降碳系统降碳量并讨论系统协同降碳驱动机制及影响因素以提出协同提升策略。论文主要从以下几个方面开展研究：

城市群“三生”协同降碳理论及方法构建。研究构建了用以指导城市群地区高效、有序降碳的“三生”协同降碳理论，协同降碳系统包括生产端降碳子系统，生活端降碳子系统以及生态端降碳子系统，系统具有协同效应，自组织效应，伺服效应以及空间效应；基于区域历史碳排放核算以及影响因素分析结果，本研究形成了能源系统模型为基础的“碳排放核算-碳排放影响因素分析-碳排放预测”的生产端和生活端碳排放预测方法模型，以地理信息系统为基础的“生态空间质量评价-用地发展模拟-碳储量评估”的生态端碳储量评估方法模型，以系统科学模型和空间计量模型为基础的“区域降碳核算-驱动机制分析-空间效应评估”的协同降碳路径研究方法体系。

京津冀城市群生产生活端碳减排情景分析。遵循“碳排放核算-碳排放影响因素分析-碳排放预测”的研究思路，利用校正后的夜间灯光影像对市级尺度碳排放进行反演和影响因素分析，结果显示多数城市过去二十年呈现人口规模、经济增长对碳排放量提升呈促进作用，能源强度下降和第三产业比例上升对碳排放提升呈抑制作用特征。研究以北京市，天津市以及河北省“十四五”规划以及区域总体规划为依据，通过调控经济发展，产业结构，可再生能源比重及能源强度，设定生产端碳排放调优化情景；通过调控人口规模及城镇化水平，设定生活端碳排放优化情景；通过调控上述各个参数，设定生产生活端碳排放双优化情景对各个城市2021-2060年碳排放量进行预测。根据未来各产业碳排放量占比，可将京津冀城市群城市进一步划分第二产业碳排放支配型城市，加工转换部门碳排放支配型城市，终端需求-加工转换部门碳排放共同支配型城市，以及各部门碳排放均衡性城市，并提出了对应的碳排放管控和削减策略。

京津冀城市群生态端碳增储情景分析。遵循“生态空间质量评价-用地发展模拟-碳储量评估”的研究思路，利用生态系统服务和权衡综合评估模型识别出京津冀城市群生境质量极高区域和较高区域大多集中于研究区西北部冀北燕山山区，冀西北坝上高原以及冀西太行山区，生境质量较低和极低区域大多位于研究区东南部城市区域；土壤侵蚀极度敏感区域和中度敏感区域主要集中在东部渤海湾区域以及西部的太行山脉东麓区域，荒漠化极度敏感区域和中度敏感区域主要集中于西北部的坝上高原，以及河北平原南部地区；将高生境质量区域和高生态敏感区域叠加后，划分出总面积为25915.60km²的生态空间质量约束型区域，约占区域总面积的12.01%。将这类区域作为生态空间质量约束型区域限制其开发，利用元胞自动机模型和系统动力学模型对未来用地类型变化情况进行模拟预测，建筑用地最大扩张面积和比例为22065.3km² 及97.88%，最小扩张面积和比例为19082.3km² 及84.65%，建筑用地扩张部分主要包括北京市、天津市外围郊区，河北南部区域以及西北部区域；以各类生态空间面积预测结果和生态空间碳储量密度为基础对不同增储情景下碳储量进行评估，尽管最优情景相较基准情景在2060年能实现31.74Tg的增储量，该情景下区域生态碳储量将从2030年的2093.26Tg 下降至2060年的2059.60Tg，下降速度约为1.122Tg/年；从城市层面来看，唐山市生态碳储量下降幅度最高，约为6.52%，保定市生态碳储量损失值最大，约为9.78Tg，其中多为耕地碳储量损失。

京津冀城市群“三生”协同降碳路径效应分析。遵循“区域降碳核算-驱动机制分析-空间效应评估”的研究思路，通过对比不同情景组合下降碳量，分析系统协同效应，结果显示天津市，石家庄市，承德市，张家口市，秦皇岛市以及邯郸市形成了“生产-生活”协同降碳效应，北京市和承德市形成了“生产-生活-生态”协同降碳效应；构建绝热近似方程和序参量演化方程，识别系统序参量及各个子系统之间的关系以分析系统伺服效应和自组织效应，2021-2040年北京市和承德市均实现了“生产-生活”，“生产-生态”以及“生活-生态”之间的协同降碳，2041-2060年新实现“生产-生活”，“生产-生态”以及“生活-生态”协同降碳的城市包括天津市，张家口市以及秦皇岛市，生产子系统是“生产-生活”和“生产-生态”复合系统中的序参量，生活子系统是“生活-生态”复合系统中的序参量，各序参量均对其他子系统有着正向的促进作用，对于复合系统协同度提高有着负面的抑制作用，反映出复合系统仍处于协同初级阶段；从系统协同得分上看，对比两阶段，承德市和秦皇岛市“生活-生产”降碳系统协同得分值，天津市，承德市，邢台市以及邯郸市“生产-生态”降碳系统协同得分值，北京市，石家庄市，承德市“生活-生态”降碳系统协同得分值均得到了显著提升。从系统空间效应来看，京津冀城市群生产端和生活端碳排放，生态端碳吸收量以及净碳排放量在未来空间分布上呈显著正相关关系，但聚集指数逐渐降低，反映出碳排放和碳吸收量随空间聚集程度逐渐降低的趋势；从净碳排放量解释变量来看，城市群生产因素和生活因素对于净碳排放起促进作用，总体呈现西南高，东北低的特征，生态因素对净排放量起抑制作用，总体呈现西南低，东北高的特征。

综上所述，本研究创新构建了适用于城市群地区的“三生”协同降碳理论，以及对应的涵盖能源系统模型，地理信息模型，系统科学模型以及空间计量模型的“三生”协同降碳研究方法体系。通过对京津冀城市群未来生产端和生活端碳排放以及生态端碳储量预测，分析了降碳系统所形成的协同效应，伺服效应，自组织效应及空间效应，并提出了“三生”协同降碳提升建议措施，研究结果有望推动区域实现多路径下高效化降碳，并为其他城市群碳达峰碳中和相关研究和实践提供借鉴。

In recent years, the frequent occurrence of risk events triggered by global climate change has posed significant threats to human survival. Excessive carbon dioxide emissions from energy consumption activities are the main causes for these climate change effects. Urban agglomerations, being the most intensely human-active areas, face challenges including the continuous increase in industrial energy consumption, the increase in carbon emissions from both production and living ends, and the decline in carbon storage capacity due to the compression of ecological spaces. Therefore, implementing the carbon reduction acts at the production, living and ecology ends contributes to the global climate change mitigation.

As one of China's greatest urban agglomerations, the Beijing-Tianjin-Hebei Urban Agglomeration (BTH) faces with severe constraints on its high-quality regional development due to excessive carbon emissions during its rapid urbanization process. Target on growing as the world-class urban agglomeration, and the pilot area for ecological restoration and environmental protection, the reduction of carbon emissions from the "production-living-ecology" (PLE) sectors is crucial for the national high-quality development. Accordingly, this study constructed the theoretical framework and research methodology for synergistic carbon reduction in these three sectors with the disciplines from synergetics, energy economics, circular economics, and spatial economics. Based on the historical carbon emissions accounting and influencing factors analysis, emission reduction scenarios were set to predict the carbon emissions in the future from production and living sectors. Furthermore, ecological spatial quality assessment and land use transformation simulation were conducted to predict carbon storage in the ecological sector under various carbon sequestration scenarios. With the principles of systemic synergy, servomechanism, self-organization, and spatial effects, this study estimated the carbon reduction amount with the PLE carbon reduction system and discussed the driving mechanisms and influencing factors of synergetic effect to propose strategies for carbon reduction. The thesis undertakes research in the following fields:

Construction of the theory and method of synergetic carbon reduction in urban agglomerations. The study developed a theory for carbon reduction in urban agglomerations, including production, living, and ecological subsystems. These subsystems form a composite symbiotic system with synergistic, self-organizing, servomechanism, and spatial effects. Based on regional historical carbon emissions accounting and influencing factors analysis, this study developed methodological models for carbon emissions predicting in the production and living sectors, ecological space carbon storage assessment methods based on Geographic Information Systems, and a synergistic carbon reduction path research methodology based on systems science and spatial econometric models

Carbon reduction scenario analysis at the production and living sectors. With the research methodology of 'carbon emission accounting - carbon emission influencing factors analysis- carbon emission prediction', this study utilizes corrected nighttime light data to invert and analyze carbon emissions at the city scale. The results reveal that in most cities, over the past two decades have seen population scale and economic growth enhance carbon emissions, whereas reductions in energy intensity and a rise in the tertiary sector have exerted a restraining influence. Subsequently, based on the results of the analysis of carbon emission influencing factors, scenarios are established, including a baseline scenario, a production structure optimization scenario, a living structure optimization scenario, and a synergistical optimization scenario. LEAP model was employed to forecast carbon emissions from 2021 to 2060 for each city. The findings indicate significant declines in carbon emissions post-2020 under the synergistical optimization scenario across all cities. Cities showing ongoing emission reductions post-2020 under the living structure optimization scenario include Chengde, Zhangjiakou, Baoding, Xingtai, and Handan; except for Tianjin, all cities demonstrate notable reductions under the production structure optimization scenario. Regarding the annual average change rate of carbon emissions, all cities exhibit negative growth in each scenario, with the most rapid decline observed in the synergistical optimization scenario, followed by the production structure optimization scenario, and then the living structure optimization scenario. Based on the carbon emissions and sectoral proportions in each city, the Beijing-Tianjin-Hebei Agglomeration can be further classified into cities with secondary industry dominated emissions, including Shijiazhuang, Qinhuangdao, Tangshan, Langfang, Hengshui, and Xingta, processing and transformation sector-dominated emissions, including Zhangjiakou and Cangzhou, and those with a joint dominance of end-demand and processing sectors, including Chengde, Baoding, and Handan, cities with balanced emissions across sectors including Beijing and Tianjin. Subsequently, tailored carbon emission control and reduction strategies are proposed, based on the socio-economic development characteristics of each city and the anticipated development levels of carbon emissions in each sector.

Ecological spatial quality evaluation and ecological carbon storage scenario analysis of the Beijing-Tianjin-Hebei agglomeration. Following the research idea of "ecological space quality assessment-land use growth simulation-carbon storage assessment", the ecosystem service and trade-off comprehensive assessment model was used to identify that most of the areas with extremely high habitat quality and high areas in the Beijing-Tianjin-Hebei agglomeration were concentrated in the Yanshan Mountains in the northwest of the study area, the Bashang Plateau in the northwest of Hebei Province and the Taihang Mountains in the western Hebei region, and most of the areas with low and extremely low habitat quality were located in the urban areas in the southeast of the study area. The extremely sensitive and moderately sensitive areas of soil erosion are mainly concentrated in the eastern Bohai Bay area and the eastern foothills of the Taihang Mountains in the west. The extremely sensitive and moderately sensitive areas of desertification are mainly concentrated in the Bashang Plateau in the northwest and the southern part of the Hebei Plain. After overlaying the high habitat quality area and the high ecological sensitive area, the ecological spatial quality constrained area with a total area of 25,915.60 km² was recognized, accounting for 12.01% of the total area. Restricting development in these areas and using cellular automata models and system dynamics models to simulate future land use changes, the maximum and minimum expansion areas and proportions of construction land are predicted to be 22,065.3 km² (97.88%) and 19,082.3 km² (84.65%), respectively. The expansion of construction land mainly includes the suburbs of Beijing and Tianjin, the southern and the northwestern region of Hebei. Based on the predicted results of various ecological spaces area and the density of carbon storage in ecological spaces, carbon storage under different sequestration scenarios is assessed. Compared with the baseline scenario, the optimal scenario achieves an increased storage of 31.74 Tg by 2060, but the regional ecological carbon storage is projected to decline from 2,093.26 Tg in 2030 to 2,059.60 Tg in 2060, with a decline rate of approximately 1.122 Tg/year. At the city level, Tangshan had the highest decline in ecological carbon storage, about 6.52%, and Baoding had the largest ecological carbon storage loss value, about 9.78Tg, of which most of them were cultivated land carbon storage losses.

Analysis of the synergistic carbon reduction pathway within the 'production-living-ecology' end of the Beijing-Tianjin-Hebei Agglomeration. With research approach that encompasses regional carbon accounting, analysis of driving mechanisms, and assessment of spatial effects, the study evaluates carbon reduction under different scenarios, focusing on systemic synergism. The findings indicate that cities like Tianjin, Shijiazhuang, Chengde, Zhangjiakou, Qinhuangdao, and Handan have established a synergistic carbon reduction effect in the 'production-living' sectors, whereas Beijing and Chengde have extended this to the 'production-living-ecology' sectors. By developing adiabatic approximation equations and order parameter evolution equations, the study identifies system order parameters and the interrelations among various subsystems, analyzing both servo-mechanism and self-organizing effects. Between 2021 and 2040, Beijing and Chengde achieved synergistic carbon reduction across the 'production-living', 'production-ecology', and 'living-ecology' sectors. From 2041 to 2060, cities including Tianjin, Zhangjiakou, and Qinhuangdao newly achieved synergistic carbon reduction across these sectors. The production subsystem is identified as the order parameter in the 'production-living' and 'production-ecology' systems, while the living subsystem plays this role in the 'living-ecology' system. Each order parameter positively influences the synergism of other subsystems and negatively influences the comprehensive system, which reflects that the synergy system was still at the primary stage. Comparing two phases, system synergy scores have shown significant improvements, particularly in the 'living-production' carbon reduction system of Chengde and Qinhuangdao, the 'production-ecology' carbon reduction system of Tianjin, Chengde, Xingtai and Handan, as well as the 'living-ecology' system of Beijing, Shijiazhuang and Chengde. From the spatial effect perspective, the production and living sectors' carbon emissions, ecological sector's carbon absorption, and net carbon emissions in the Beijing-Tianjin-Hebei Agglomeration are significantly positively correlated in future spatial distribution. However, the aggregation index decreases over time, reflecting a trend of diminishing carbon emissions and absorption intensity with lower spatial aggregation. In terms of explanatory variables for net carbon emissions, production and living factors in the agglomeration enhance net emissions, typically higher in the southwest and lower in the northeast, while ecological factors restrain emissions, presenting opposite geographical characteristics.

In summary, this study innovatively constructs a synergistic carbon reduction theory tailored to urban agglomerations, encompassing a methodological system that includes energy system models, geographic information models, system science models, and spatial econometric models. By predicting future carbon emissions from the “production-living” sector and carbon storage in the ecological sector of the Beijing-Tianjin-Hebei Agglomeration, the study analyses the synergistic, servo-mechanism, self-organizing, and spatial effects of the carbon reduction system. It also proposes strategies for enhancing synergistic carbon reduction, aiming to facilitate efficient carbon reduction in the region and provide insights for carbon peak and carbon neutrality research and practices in other urban agglomerations.