China has made remarkable achievements in reducing carbon emissions in recent years. However, there is still much reduction room before achieving carbon neutrality. In Beijing, the capital of China, it is a strategic choice to respond to global climate change by promoting green and low-carbon development. This paper calculates the carbon dioxide emissions of key industries in Beijing and analyzes the temporal evolution trend of carbon emissions. Carbon dioxide emissions in Beijing before 2030 are predicted based on the grey prediction GM (1,1) and BP neural network model. The effects of factors of carbon dioxide emissions are discussed using the threshold regression model under different economic conditions. The results show that energy consumption intensity, GDP per capita, and the ownership of civil cars have a positive impact on carbon dioxide emissions, while the number of permanent residents and urban green space areas have a negative impact on carbon dioxide emissions. These findings of carbon emission prediction and influencing factors contribute to carbon reduction path design. Related policy implications on carbon emission reduction are put forward from the aspects of promoting industrial upgrading, accelerating the construction of advanced economic structures, optimizing transportation structures, and strengthening green building development.

In order to balance the economic development and ecological impact of tourism, it is essential to study tourism eco-efficiency in the context of sustainable development. This study analyzed regional tourism eco-efficiency based on the panel data of the 13 cities of the Beijing–Tianjin–Hebei region using the super-SBM DEA model. Then, we analyzed the driving factors, compared regional differences, and investigated influencing factors of tourism eco-efficiency by applying the global Malmquist–Luenberger (GML) index, Theil index, and geographically and temporally weighted regression (GTWR) models. The results demonstrate the following: (1) The overall tourism eco-efficiency in the Beijing–Tianjin–Hebei region between 2010 and 2019 was low, but it had an increasing trend. (2) The advancement of technological progress factors was mostly responsible for the increase in tourist eco-efficiency. (3) The results for tourism eco-efficiency were significantly polarizing, but the gap among the 13 cities is gradually narrowing. Regional differences are the main contributors to differences in tourism eco-efficiency. (4) Per capita GDP, the proportion of tertiary industry in GDP, the number of patents granted, and the proportion of urban population in the total population were the main factors affecting tourism eco-efficiency. This study could serve as a model for similar countries and regions seeking to enhance tourism eco-efficiency and achieve the Sustainable Development Goals.

The high-quality economic growth of tourism is intimately related to a city’s overall strength, and urban resilience is an important index to measure the comprehensive strength of a city. Therefore, determining how to enhance the construction of urban resilience, improve the quality of tourism development, and promote the coupling coordination of these two systems has attracted academic attention in recent years. Based on the panel data of 13 cities in the Beijing–Tianjin–Hebei region from 2010 to 2021, an entropy weight method, coupling coordination model, and obstacle degree model were used to analyze the coupling coordination degree, spatiotemporal evolution characteristics, and obstacle factors between urban resilience and the tourism economy. The results show the following: (1) Urban resilience and tourist economic development levels in the Beijing–Tianjin–Hebei region show an overall upward trend, and both of them show obvious spatial differences. (2) The coupling coordination degree of urban resilience and the tourism economy shows a trend of first rising and then declining in the temporal dimension, while it shows a spatial differentiation pattern of “high in the middle and low in the surrounding area” in the spatial dimension. (3) The obstacle degree structure of the coupling coordination of urban resilience and the tourism economy in the Beijing–Tianjin–Hebei region is relatively stable, with economic resilience as the leading obstacle in the urban resilience system and tourist economic development benefits as the leading obstacle in the tourism economy system. In the future, the Beijing–Tianjin–Hebei region needs to strengthen regional cooperation, enhance the driving role of central cities, continuously improve urban resilience, and promote the high-quality development of the tourism economy.

Under China’s “dual-carbon” goal, it is necessary to coordinate the relationship between economic growth and emission reduction. Based on the panel data of 30 provinces in China from 2011 to 2021, this paper explores the coordination level among carbon cutting (CC), pollution reduction (PR), and economic growth (EG) by using the coupling coordination degree (CCD) model, a cold and hot spot analysis, and the Dagum Gini coefficient. Furthermore, we analyze the influencing factors of CCD from a spatial perspective using the geographically weighted regression (GWR) model. The results show that the coordination level of CC, PR, and EG in China has continued to improve and entered a moderately coordinated stage. Meanwhile, regional differences are also evident. The eastern region is a high-CCD concentration area, while the northwest and northeast regions are low-CCD concentration areas. However, inter-regional differences in CCD are decreasing. Urbanization, foreign direct investment, and new quality productive forces contribute significantly to achieving synergies among CC, PR, and EG. However, the effect of industry digitization on CCD fails the significance test in most provinces. The effects of the factors on CCD exhibit obvious spatial heterogeneity characteristics. These findings can provide an important basis for the formulation of regionally differentiated green and low-carbon development policies.

Achieving green development demands simultaneously balancing between economic growth and pollutant emission reduction, which can have complex impacts on regional disparities. This study measures the green development efficiency (GDE) for 266 Chinese cities during the 11th–13th National Five-Year Plan periods (2006–2020) by a global non-oriented endogenous directional distance function (endogenous DDF) that endogenizes direction vectors using the maximum improvement potential. Regional disparities are then quantified and decomposed by the Dagum Gini coefficient decomposition (intra-group, net inter-group, and hypervariable density) across seven regions. To make the policy implications operational, we further derive the separable economic efficiency loss and environmental efficiency loss components from the endogenous DDF and identify cities’ optimal preference options (γ); scenario experiments (5–30% improvement) are used to validate whether differentiated improvement directions can simultaneously raise GDE and narrow disparities. The main findings are as follows: (1) From the 11th Five to the 13th Five, cities’ GDE evolved from a median-centered distribution to a bimodal distribution, accompanied by spatial polarization and widening regional disparities. (2) By the 13th Five period, regional disparities had deepened, mainly driven by inter-group differences, notably hypervariable density. The original regional development patterns were disrupted, leading to increased overlap across cities. (3) Most cities have shifted from a “green-oriented” to an “economic-oriented” development preference since the 13th Five period. North China, South China, and East China favor pollution reduction, while others prioritize economic growth. (4) Preference options for cities with varying resource endowments should adapt over time. Under various hypothetical scenarios, cities adopting differentiated optimal options can enhance their GDE while simultaneously narrowing regional disparities. Reducing arbitrariness in balancing emission reduction and economic growth can promote regionally coordinated and environmentally sustainable development.

Understanding the synergistic effects of pollution reduction (PR) and carbon mitigation (CM) and their driving factors is essential for achieving environmental improvement and dual-carbon targets. On the basis of panel data from 13 cities in the Beijing–Tianjin–Hebei (BTH) urban agglomeration from 2010 to 2023, this study analyzed the spatiotemporal evolution of air pollutant and carbon emissions. The synergistic effects of PR and CM were quantified using the co-control effect coordinate system and vector angle analysis, and their underlying driving mechanisms were examined using a geographically and temporally weighted regression model. Results showed that air pollutant emissions in the BTH region declined substantially over the study period, whereas carbon emissions increased in all cities, except Beijing. The spatial patterns of air pollutant and carbon emissions were largely consistent, with Tangshan being a high-emission hotspot and northern Hebei cities being low-emission areas. Most cities were in a “pollution reduction but carbon increase” stage, but the overall synergistic degree gradually improved. The synergistic effects were positively driven by green travel and technological R&D and negatively influenced by economic development, energy utilization, and transportation structure. The positive effect of industrial structure on PR and CM weakened, and spatial heterogeneity was evident. Economic development and technological R&D exerted strong influences in southern Hebei. Energy utilization and transportation structure had pronounced effects in northern Hebei. Industrial structure had remarkable effects in cities surrounding Beijing and Tianjin. Moreover, green travel demonstrated spatial heterogeneity, exerting a facilitative effect on emissions in southern Hebei cities. These findings provide policy implications for promoting the synergistic effects of PR and CM in the BTH urban agglomeration.