Search

The reasonable spatial layout of industries is crucial to carbon reduction and high-quality economic development. This paper establishes a research paradigm for optimizing the industrial spatial layout and high-quality development in the context of carbon peaking. Based on the perspectives of industrial transfer, the static agglomeration index, dynamic agglomeration index, industrial gradient coefficient, and low-carbon competitiveness index are used to analyze industrial agglomeration, competition status, and low-carbon competitiveness. Taking the Great Bend of the Yellow River (the Bend) as an example, we analyze the current situation in industrial development, guide the orderly transfer of industry, and optimize the spatial layout of industries to achieve high-quality economic development. The results show that resource- and capital-intensive industries have obvious advantages in agglomeration, competitive edge, and low-carbon competitiveness, while labor- and technology-intensive industries have weak advantages. The spatial layout of agglomerated industries was analyzed across four types of factor-intensive industries; these industries are the focus of industrial layout in the Bend. Promising industries were observed in all types of factor-intensive industries except capital-intensive industries, and these industries should be cultivated carefully in all provinces. Scale industries were mainly observed in resource- and capital-intensive industries; these industries should be transformed and upgraded to control the total amount and intensity of carbon emissions. The study’s findings provide a basis for optimizing the spatial layout of industries and reducing carbon emissions through industrial transfer in the context of carbon peaking. The relevant industries should be transformed and upgraded to control the total amount and intensity of carbon emissions.

The development of digital technology provides new governance methods for achieving the goal of “carbon peaking and carbon neutrality”. Since 2013, the pilot construction of smart cities in China has strengthened the government’s digital governance capabilities and significantly influenced the reduction in carbon emissions. This paper provides empirical evidence for the driving effect of digitization on carbon emission reduction based on panel data from 353 cities in China. The results show that digital governance based on smart city construction pilots has significantly reduced regional carbon emissions, and the implementation of smart city construction pilots has reduced regional carbon emissions by an average of 6.6%, and this effect is sustainable over the long term. The increase in the level of digitalization has significantly promoted carbon emission reduction. From the perspective of the impact path, regional green patent innovation has played a significant partial intermediary effect in the process of digitization-driven carbon emission reduction. From a micro-mechanism standpoint, digitization plays a significant role in promoting the green innovation of high-polluting listed companies.

In the process of economic development, the consumption of energy leads to environmental pollution. Environmental pollution affects the sustainable development of the world, and therefore energy consumption needs to be controlled. To help China formulate sustainable development policies, this paper proposes an energy consumption forecasting model based on an improved whale algorithm optimizing a linear support vector regression machine. The model combines multiple optimization methods to overcome the shortcomings of traditional models. This effectively improves the forecasting performance. The results of the projection of China’s future energy consumption data show that current policies are unable to achieve the carbon peak target. This result requires China to develop relevant policies, especially measures related to energy consumption factors, as soon as possible to ensure that China can achieve its peak carbon targets.