Drivers and regional heterogeneity of carbon emission predictions under dual carbon goals in the middle and lower reaches of the Yangtze River

Carbon Emission Prediction in the Middle and Lower Reaches of the Yangtze River: Drivers and Regional Heterogeneity under Carbon Neutrality Goals

Against the backdrop of increasingly severe climate change, achieving carbon neutrality has become an urgent goal for developing countries worldwide. For a rapidly developing economy like China, effectively controlling carbon emissions while maintaining economic growth holds significant strategic importance. The Chinese government has pledged to peak carbon emissions before 2030 and strive for carbon neutrality before 2060. However, translating macro-level goals into actionable regional policies requires scientifically quantifying the carbon emission characteristics of different regions and deeply understanding the complex factors driving these changes.

Industry Background: Policy Drivers and Regional Differences

The global consensus in climate governance is that effective policy incentives and technological innovation can accelerate the low-carbon transition. The international community has formulated various policies to promote carbon reduction, but existing policies often focus on macroeconomic models, failing to fully capture the nuances of regional economic structures, industrial layouts, and energy consumption patterns. As research indicates, the significant differences among provinces in China in terms of economic development levels, industrial structure, and energy consumption patterns directly determine the baseline and reduction potential of their carbon emissions. Therefore, a single national reduction strategy is difficult to implement precisely.

The middle and lower reaches of the Yangtze River, as a vital economic hinterland and densely populated area of China, hold a core position in the national carbon neutrality strategy. Regional policies, such as the "Yangtze River Economic Belt Development Plan" and the "14th Five-Year Plan," explicitly place ecological protection and green transformation at the strategic core, making carbon emission predictions for this region an important window to test policy effectiveness.

Current Development Dynamics: Evolution of Model Methods

Traditional carbon emission prediction models are often too simplified and fail to comprehensively reflect the coupled effects of multiple driving factors. To overcome this limitation, this study adopts an innovative methodology, combining the extended STIRPAT model with Partial Least Squares (PLS) regression. This method allows researchers to systematically incorporate a series of socioeconomic and environmental driving factors and use scenario simulations to compare the differences in carbon emission trajectories under different drivers.

The focus of the research is on identifying which regional factors contribute most to carbon emissions. Through simulations of multiple provinces (such as Hubei, Hunan, and Jiangxi), the study reveals significant regional heterogeneity. Specifically, there are notable regional differences in the peak time of emissions, the difficulty of the carbon neutrality path, and the relative importance of driving factors among different provinces.

Impact on Energy Systems: Foundation for Precision Policy

Accurate carbon emission prediction has profound guidance for the energy system. It not only helps assess the stability and sustainability of the existing energy supply but also directly influences expectations regarding energy security and electricity costs. When prediction models reveal structural bottlenecks in the reduction paths of certain regions, regulatory bodies can deploy targeted intervention measures in advance, such as optimizing the energy structure, accelerating industrial upgrading, or strengthening investment in grid upgrades.Furthermore, from the perspective of the industrial chain, carbon emission forecasts also provide demand signals for the deployment of green technologies (such as clean energy and energy storage). Regional emission characteristics can guide investment institutions and policymakers to direct limited green investment resources towards areas with the greatest emission reduction potential, thereby accelerating the energy transition process.

Challenges Faced: Structural Constraints and Policy Uncertainty

Despite continuous progress in research methods, achieving carbon neutrality goals still faces multiple challenges. First is whether the deployment speed and scale of energy storage systems can keep up with the penetration rate of renewable energy, which is one of the key constraints affecting grid stability and carbon emissions. Second are the bottlenecks of transmission network limitations and technological maturity, especially in developing long-term infrastructure requiring extensive planning, such as offshore wind power or large-scale pumped hydro storage, which remain constraining factors for regional emission reductions.

Furthermore, project financing pressure and policy uncertainty still exist. For energy infrastructure construction requiring significant upfront capital investment, an unstable regulatory environment may lead to project delays or abandonment. Finally, balancing the relationship between economic growth needs and carbon reduction targets, especially in underdeveloped regions, requires more refined regional economic development models to guide energy structure optimization.

Future Outlook: The Necessity of Region-Specific Strategies

Looking ahead 5 to 20 years, the energy structure will exhibit a highly regionalized characteristic. With technological advancements, such as the maturation of the hydrogen industry and battery technology, the penetration rate of clean energy will continue to rise. However, the future competitive landscape will no longer be a simple race for total volume, but rather a competition between regions in terms of technological application efficiency, policy innovation, and green finance attractiveness.

A successful energy transition will depend on collaborative strategies at the regional level. This means policymakers need to shift from "one-size-fits-all" macro directives to "tailor-made" regional adaptive strategies. For example, in provinces with different resource endowments and industrial structures, differentiated emission reduction pathways should be adopted, supporting industrial upgrading in high-carbon emission areas while accelerating the deployment of clean energy in low-carbon potential areas.

Overall, scientific carbon emission forecasting tools will become the cornerstone for guiding the flow of green investment. The inflow of ESG capital will increasingly favor enterprises that can clearly demonstrate their emission reduction pathways and regional adaptability strategies. Only by combining the insights from scientific forecasts with planning for energy infrastructure construction at the forefront, can we ensure that while achieving China's carbon neutrality strategy, we maintain economic vitality and reach sustainable clean energy goals.