Social progress and economic development has resulted in the need to focus on the impacts of building microenvironment on the energy consumption in office buildings. The concept of a building’s microenvironment was introduced to understand the local microclimate around a building that is formed by the surrounding urban green spaces, the distribution of roads, and building proximity. For this research, we adopted a regression analysis to quantify the impacts of building microenvironment on energy consumption in office buildings. Taking the government office buildings of Guangdong Province as an example, we measured the building microenvironment through the urban green space density, road density, and number of points of interest (POI) around the buildings. The results showed that when the green space density increased by one unit, the energy consumption in government office buildings was reduced by 0.277%. Moreover, an increase of 1% in road density and in the number of POI increased the energy consumption in government office buildings by 0.288% and 0.048%, respectively. Furthermore, we discussed the heterogeneous impacts of building microenvironment on the energy consumption in government office buildings at varying scale levels. Green space and road density had less impact on the energy consumption in larger buildings, whereas the number of POI had no significant impact on small-scale buildings but did have a significant impact on large-scale buildings. There were also some limitations in the study. The data were limited to government office buildings, and did not include panel data, as well as it lacked building characteristics such as orientation, floor height, and building materials. In addition, it was impossible to evaluate the impacts of meteorological factors such as wind speed and thermal radiation on energy consumption in buildings. Nonetheless, our study demonstrates that energy-aware urban planning and design have the potential to unlock energy efficiency for cities worldwide.

Urban agglomerations, as hubs of population, economic activity, and energy consumption, significantly contribute to greenhouse gas emissions. The interconnected infrastructure, energy networks, and shared economic systems of these regions create complex emission dynamics that cannot be effectively managed through isolated city-level strategies. However, these regions also present unique opportunities for innovation, policy implementation, and resource optimization, making them crucial focal points in efforts to reduce carbon emissions. This study examines China’s three major urban agglomerations: the Yangtze River Delta, the Pearl River Delta, and the Beijing–Tianjin–Hebei region. Utilizing data from 2005 to 2020 and a comprehensive evaluation model (BCPCAM), the research offers more profound insights into the socio-economic factors and collaborative mechanisms influencing emission trends, facilitating the development of targeted strategies for sustainable development and carbon neutrality. The findings indicate that (1) economic development and carbon control can progress synergistically to some extent, as economically advanced cities like Beijing and Shanghai have achieved their carbon peaks earlier; (2) natural resource endowment significantly affects urban carbon emissions, with resource-rich cities such as Tangshan and Handan, where fossil fuels dominate the energy mix, facing considerable challenges in reducing emissions; and (3) notable differences exist in the growth patterns of carbon emissions between urban and rural buildings, underscoring the need for tailored carbon reduction policies.

Regional industry synergistic planning contributes to the achievement of Goal 11 of the United Nations’ Sustainable Development Goals. The reasonable layout of regional industries is an important measure for achieving sustainable development through distinguishing between different industries. Taking into account the competitive and cooperative relationships between regional cities, this study created a Stackelberg model considering information distribution and fairness preferences. It analyzed the industrial selection strategies and influencing factors under the requirements of output maximization and profit maximization between regions. The model discussions and numerical simulation results showed that regional industrial planning and adjustment should consider both internal and external behavioral factors. The impact of information distribution and fairness preferences on the selection of heterogeneous and homogeneous industries varies. Differentiated industrial selection should be conducted based on output maximization or profit maximization. Furthermore, following cities should take the initiative to integrate into the industrial development plans of nearby large cities, and leading cities should effectively layout and optimize regional productivity. Both industrial planning and choice require the establishment of regional coordination mechanisms. By enhancing the level of mutual trust and reciprocity among cities, reducing information asymmetry, and guiding fairness preferences, regional industrial synergy is promoted, and conditions are created for sustainable industrial development.

Existing studies on embodied carbon in buildings have mainly focused on total accounting or single intensity indicators, with limited attention to long-term material accumulation, annual incremental embodied carbon, and inter-city differences in carbon lock-in risk. This study examines the Guangdong–Hong Kong–Macao region, comprising 21 prefecture-level cities in Guangdong Province together with Hong Kong and Macao. Based on 1 km gridded building material stock data from 2010 to 2020, we combined stock-difference and embodied carbon accounting, spatial autocorrelation and hotspot analysis, population- and economy-based intensity assessment, and urban typology identification to reveal the spatiotemporal differences in material accumulation, annual incremental embodied carbon, and carbon lock-in risk. The results show that cumulative building material stocks increased by 19.41% from 2010 to 2020, while annual incremental embodied carbon declined from 100.60 Mt CO₂ in 2011 to 24.34 Mt CO₂ in 2020, a decrease of 75.81%. This indicates that continued long-term material accumulation coexisted with a substantial decline in current incremental carbon pressure. Spatially, annual incremental embodied carbon contracted from continuous high-value belts to a small number of localized growth nodes, forming a clear “contraction–concentration” pattern. Clear mismatches were also observed among population-based intensity, economy-based intensity, and local hotspots, indicating that lower citywide average intensity does not necessarily imply lower carbon lock-in risk. By further integrating existing stocks, lock-in formation pressure, and efficiency burden, the 23 cities were classified into five combinations of carbon lock-in risk. These findings suggest that regional low-carbon governance of buildings should move beyond a singular focus on new construction and toward lifecycle-based differentiated management that jointly addresses existing stock renewal, remaining incremental pressure, and local hotspots.