西北地区双碳目标动力学研究
Study on Dynamics of Double Carbon Target in Northwest China

本研究通过统计分析西北区域数据，构建碳排放量综合评价体系，从多角度、多尺度解析西北区域碳排放量因子的定量变化。综合运用机理模型、机器学习和统计模型等方法，旨在为制定减排政策和措施提供科学依据，支持实现生态可持续发展的深层目标。本研究基于西北地区碳排放的来源和总量调查，分析了不同因素对该地区碳排放量的影响。首先，通过分类筛选基础数据，将影响碳排放的因素划分为三级指标，构建了反映西北区域经济、人口、能源和产业等要素的动力学结构体系。在此基础上，建立了CECEI模型来量化碳排放综合指数，明确了各指标之间的潜在关联性。通过对西北区域数据进行产业化和能源化情景划分，运用灰色关联分析和Pearson相关性分析等方法，系统评估了各因素对碳排放的影响，量化了其重要性和贡献度。在能源消费量预测方面，本研究构建了基于人口和经济变化的预测模型。通过对西北区域数据进行整合处理，分别建立了基于Logistic回归和ARIMA模型的人口和经济预测模型。考虑多参数之间的相互作用，建立了参数方程，并采用粒子群优化算法进行参数优化。基于人口预测模型和GDP预测模型的双输入方式，构建了PSO-STIRPAT方法，实现了兼顾搜索效率和精度的分时期能源消费量预测。进一步考虑间接碳排放结构体系，深入挖掘多参数之间的非线性关系，构建了基于LSTM的长时序多变量预测模型，模型步长设置为5，以高效预测西北区域碳排放量的非线性变化。
In this study, a comprehensive evaluation system of carbon emissions is established by statistical analysis of the data in Northwest China, and the quantitative changes of carbon emission factors in Northwest China are analyzed from multiple angles and scales. The comprehensive application of mechanism model, machine learning and statistical model aims to provide a scientific basis for formulating emission reduction policies and measures and support the deep goal of ecological sustainable development. Based on the investigation of the source and total amount of carbon emissions in Northwest China, this study analyzes the influence of different factors on carbon emissions in this area. Firstly, by sorting and screening the basic data, the factors affecting carbon emissions are divided into three levels of indicators, and a dynamic structure system reflecting the factors such as economy, population, energy and industry in the northwest region is constructed. On this basis, the CECEI model is established to quantify the comprehensive index of carbon emissions, and the potential correlation between the indicators is clarified. By dividing the data of Northwest China into industrialization and energy-based scenarios and using the methods of gray correlation analysis and Pearson correlation analysis, this paper systematically evaluates the influence of various factors on carbon emissions and quantifies their importance and contribution. In the aspect of energy consumption prediction, this study constructs a prediction model based on population and economic changes. By integrating Northwest China data, the population and economic forecasting models based on Logistic regression and ARIMA model are established respectively. Considering the interaction between multiple parameters, the parameter equation is established, and the particle swarm optimization algorithm is used to optimize the parameters. Based on the dual input mode of population forecasting model and GDP