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Even though renewable energy development has gained momentum in China, thermal power generation still accounts for approximately 70% of the county's total power generation serving as the major source of carbon dioxide (CO 2) emissions in China. Facing the challenges of meeting 2030 peak target of CO 2 emission and realizing the coordinated development of thermal power generation in Beijing-Tianjin-Hebei region, this paper applies generalized Divisia Index Method (GDIM) to decompose the dynamics in the relevant CO 2 emission. The effects of five factors including electricity demand, energy consumption, technology, energy efficiency and energy-mix are considered. The decomposition suggests that electricity demand is the primary factor driving the CO 2 emission up, whereas technology effect decreases CO 2 emission the most. Given the significant roles of technology, energy-mix and energy efficiency in CO 2 emissions reduction, seven scenarios are designed to identify the optimal coordinated development pathway for thermal power generation in Beijing-Tianjin-Hebei region. Through upgrading energy structure and/or enhancing energy efficiency, the thermal power generation in Beijing-Tianjin-Hebei region can achieve coordinated development and realize the 2030 peak target under four scenarios. The detailed development pathways for CO 2 emissions and specific policy implications for Beijing, Tianjin and Hebei are provided to further govern CO 2 emissions and maintain sustainable development. • Proposed a five-factor Generalized Divisia Index Model (GDIM) for CO 2 emission analysis. • Analyzed the CO 2 from thermal power generation in China's Beijing-Tianjin-Hebei region. • Electricity demand and technology contributed the most to the positive and negative changes of CO 2 , respectively. • Designed seven thermal generation development scenarios to study the CO 2 change until 2030. • The 2030 CO 2 peak target can be achieved under four scenarios. [ABSTRACT FROM AUTHOR]

• Water use efficiency evaluation based on 3D water footprint using SBM model. • Two scenarios for water use efficiency evaluation. • Analyzing the driving forces affecting water footprint change using the LMDI model. Water shortage and the conflict between water supply and demand have become the main constraints to the sustainable development of sustainable societies in Beijing-Tianjin-Hebei (BTH) region. Scientific evaluation of water usage efficiency and clarification of the three-dimensional water ecological footprint (EF 3D) drivers are of great significance for the collaborative environmental management of BTH region. This paper firstly explores the evolution of water footprint breadth (EF size), depth (EF depth) and EF 3D in BTH region from 2004 to 2020. Using the super-efficient SBM (Slacks-Based Measure) model with EF 3D to evaluate the water use efficiency of BTH region in two scenarios. 22 impact factors were selected to analyze the dynamics affecting the change of EF 3D in BTH region by using the LMDI (Logistic Mean Divided Index) model. The results show that: (1) The water resources pressure of the three provinces are in the overload state from 2004 to 2020, and the EF 3D is at its maximum in 2011–2015. The EF depth of BTH region is fluctuating up and then fluctuating down around 2012, and the most serious capital consumption of water resources stock is in Tianjin. (2) The water resources in Hebei are all in inefficient use. The water resources in Tianjin are in a state of inefficient use in some study years, and since 2014, the water resources are in a state of fluctuating increase and high efficiency. As for Beijing, the water resources are in a state of efficient use in the study years. On the whole, scenario 2 better reflects the water resources utilization efficiency situation and is more in line with the real situation. (3) The water structure and endowment of water resources are pivotal factors positively influencing the EF 3D. To mitigate EF 3D and achieve sustainable utilization of water resources, both Hebei and Tianjin have ample room for progress in elevating agricultural water-saving technologies, optimizing industrial structure, and enhancing water usage patterns. Agricultural water consumption is no longer the primary driver of water resource depletion in Beijing, necessitating focused attention on the impact of external water diversions in terms of local water displacement, optimization of water usage structures, and their role in supporting socio-economic development. Additionally, excessive reliance on external water diversions may entail associated risks that warrant consideration. [ABSTRACT FROM AUTHOR]

• Studying on dynamic ecological footprint (EF) in Jing-Jin-Ji from 1996 to 2020. • Six impact factors for driving EF is decomposed. • The EF in Jing-Jin-Ji will increase of 10% from 2015 to 2020. The ecological footprint (EF) describes the complex relationship between the eco-environment and economic development. EF dynamics can better reflect the appropriation of natural resources in various countries or regions compared with previous studies. This paper identified six dominant factors including population, per capita GDP, three major industrial added values, and energy consumption using grey correlation model for the EF changes of the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region from 1996 to 2015. Then, we predicted the EF of the Jing-Jin-Ji region from 2016 to 2020 based on support vector machine model. (1) Since 1996, the EF and per capita EF of the overall Jing-Jin-Ji region have increased, of which those of Hebei Province and Tianjin increased while Beijing decreased. (2) Energy consumption dominated the EF of Jing-Jin-Ji region. The population of Beijing had a high correlation coefficient of 0.735 with the local EF, and the degree of correlation between the EF and per capita GDP was 0.812 in Hebei Province. In Tianjin, the added value of the tertiary industry was closely correlated with its EF, with a correlation coefficient of 0.741. (3) The EF of the Jing-Jin-Ji region will reach 778.30 million hectares by 2020, an increase of 10% compared with the value in 2015. Finally, suggestions for the development of industrial structure and energy consumption are listed for the sustainable development of Jing-Jin-Ji region. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to build an objective and effective environmental performance evaluation model of air pollution control. Based on the severe air polluted region in China, that is Beijing-Tianjin-Hebei region, this evaluation model consists of four parts, including economic benefits, public service, government management and development potential, and uses the balanced scorecard theory. The weights of the indexes are measured by comprehensive evaluation method, which combined entropy weight method and analytic hierarchy process (AHP). To test the applicability of the evaluation method to different stages, back propagation (BP) neural network is introduced to simulate air pollution control in the future. The results reveal that the environmental performance of air pollution control in Beijing had an upward trend during 2010–2016, and the trend would continue in the next five years. The environmental performance of Tianjin continuously increased, especially from 2014 to 2016. Tianjin has great potential of air pollution control in the future. The environmental performance of air pollution control in Hebei fluctuated from 2010 to 2016 and it dropped to the lowest level in 2015, which mainly due to the large increase in number of days with air quality levelⅡor above compared with 2014 and the nonsignificant growth in the amount of expenditure on atmospheric prevention and control, the amount of investment in the control of industrial pollution sources and the number of law enforcement personnel. Moreover, the environmental performance of air pollution control closely related with industrial structure, especially with the proportion of the tertiary industry. The upgrading of urban industrial structure and the coordinated development of green industry should be implemented to restructure the environmental transformation in air-polluted region. Image 1000 • The traditional Balanced Scorecard is revised and applied into the environmental performance evaluation. • The effective control of vehicle emission directly determines the air quality of Beijing. • A fundamental improvement in air quality in Hebei requires a large amount of environmental investment. • The environmental law enforcement is an important guarantee for air quality improvement of Tianjin. [ABSTRACT FROM AUTHOR]