Research on the scenario prediction and classification of emission reduction strategy of carbon emission in china's oil and gas industry

Abstract

With the introduction of the "dual carbon" goal, the pathway for carbon reduction in China's energy system is gradually unfolding. This paper focuses on the oil and gas industry, one of the core sectors contributing to carbon emissions, as the research subject. Initially, a PSO-BP neural network model is employed to estimate the carbon emissions of the oil and gas industry (CEOG), including total carbon emissions (TCEOG) and carbon intensity of the oil and gas industry (CIOG). Subsequently, the integration of potential index and K-Means clustering method is applied to classify the prediction of CEOG, and corresponding emission reduction strategies are proposed based on the classification results. The findings are as follows: (1) A peak in provincial TCEOG by 2030 can only be achieved under a low-carbon scenario. (2) The provinces whose emission reduction potential index ranks the top 5 are the economically developed eastern coastal regions, while the bottom five are the less developed energy-producing regions. Based on a total amount-efficiency model, CEOG is categorized into four groups: HE-HE (high emission-high efficiency), HE-LE (high emission-low efficiency), LE-HE (low emission-high efficiency), and LE-LE (low emission-low efficiency). (3) The comprehensive CE reduction potential and total amount-efficiency results classify the future CEOG scenarios for 30 provinces in China into ten categories for the first time. Based on the actual development status of each province, tailored CE reduction strategies for the oil and gas industry should be formulated for each category, providing a practical, detailed, and quantifiable theoretical basis for the development and adjustment of national, provincial, or regional carbon reduction policies.