The Optimization of Industrial Structure Under the ‘Dual Carbon’ Goal via Multi-Objective Programming Model: Evidence from Guangdong Province, China

With the increasing frequency of extreme weather events, global concerns regarding climate change have intensified, with carbon dioxide widely recognized as the primary driver of global warming and climate disruption. It is necessary to investigate how to develop industries to meet the constant GDP growth and minimum carbon emissions. This study investigates the optimization of industrial structure under China’s ‘Dual Carbon’ Goal in Guangdong Province from 2012 to 2017, employing a multi-objective programming model. Using the input–output table, carbon emissions across 42 industries are calculated based on the Intergovernmental Panel on Climate Change (IPCC) carbon emission factor method. According to Hirschman’s theory of industrial interdependence, the economic and carbon emission linkage coefficients between these industries are obtained by calculating the Ghosh inverse matrix and the Leontief inverse matrix to analyze the economic forward and backward linkage of the industries, as well as the carbon emission forward and backward linkage. The impact of industry input and output on the urban economy and the resulting carbon emission problems are discussed, and industries are divided into encouraged and restricted industries. Using a multi-objective programming model, the expected final demand, changes in final demand, and expected carbon emissions of these industries under the ‘Dual Carbon’ Goal, with the target of maintaining the same economic growth rate and promoting carbon reduction, are analyzed. The results show that most industries in Guangdong Province need to reduce final demand, including the highest carbon-emitting industries and industries that are relatively restricted by scale in development. The policy implications of optimizing the industrial structure to reduce carbon emissions are provided.