Revealing the spatio-temporal characteristics and impact mechanism of carbon emission in China's urban agglomerations

The Temporal–Spatial Evolution Characteristics and Influential Factors of Carbon Imbalance in China

“…Therefore, we have extended the traditional IPAT framework beyond population size (PS) and economic growth (EG) by incorporating industrial structure (IS), energy efficiency (EE), and electricity structure (ES) into this framework. Equation (9) in our study encompasses these variables, reflecting technological factors within the changes in economic growth and energy efficiency.…”

“…At the same time, we assume that regional carbon imbalance is simultaneously influenced by multiple factors within the region and neighboring areas. Therefore, based on Equation (9), spatial effects are introduced to unveil the primary factors influencing carbon imbalance at the provincial level in China from a spatial perspective. To maintain generality, we have constructed the spatial Durbin model as depicted in Equation (10): (10) where i represents the province, j stands for the year, w ij signifies the (i, j) element in the spatial adjacency matrix (if two provinces are adjacent, the value is 1; otherwise, it is 0), ρ is the coefficient of spatially lagged dependent variables, β represents the coefficients of influencing factors, θ is the coefficient of lagged explanatory variables, individual fixed effects δ i control for provincial characteristics that do not vary across individuals, time fixed effects ς t encompass factors that do not change across time periods, and ε it represents the disturbance term following a normal distribution.…”

“…The existing literature has extensively delved into the spatiotemporal evolution characteristics of carbon emissions [3][4][5][6] and their intricate impact mechanisms [7][8][9]. The study in [10] examined the potential influences of energy poverty, renewable energy consumption, GDP, natural gas consumption, and trade freedom on carbon emissions, concluding that prioritizing the reduction of energy poverty in developing countries is pivotal for achieving sustainable development goals.…”

Towards Carbon Neutrality in Agglomeration: Impact of Eco-Industry Development on Urban Carbon Emission Efficiency

“…Industrial value added (IAV) [41,52] and industrial enterprise profits (IEP) [43] serve as indicators of industrial structure, while the ratio of the sum of value added in the secondary and tertiary industries to the administrative area assesses economic agglomeration (EA) [45,48,58]. Additionally, the logarithm of non-agricultural output to administrative area is utilized to measure industrial agglomeration (PA) [54,55,67] effects. Technological innovation (TI) [68,69] is gauged by the number of patented inventions, green innovation (GI) [51,57] by the quantity of green patents, and digital innovation (DI) [70] by the number of patents related to the digital economy.…”