The Paris Agreement aims to limit global warming to below 2°C, requiring ambitious carbon neutrality targets globally. Accelerating renewable energy penetration is crucial for climate mitigation. However, global decarbonization is lagging, and current policies likely lead to 2.8°C warming by the end of the century. The 27th Conference of the Parties (COP27) recommended substantial investments in renewables, but detailed allocation strategies remain unclear. China, a major emitter, aims for carbon neutrality by 2060, requiring scaling up PV and wind power significantly. Existing projections, however, fall short of this target due to factors like decreasing subsidies, insufficient transmission infrastructure, and land-use restrictions. This research addresses these challenges by employing a spatially explicit optimization model to assess the potential of PV and wind power in achieving China's carbon neutrality goals, considering geographical constraints, infrastructure limitations, and power demand flexibility.

Previous studies have modeled China's energy transition, but often lack spatial detail or fail to fully account for factors like power-load flexibility and learning dynamics. While some models exist for Europe and the USA that incorporate spatial heterogeneity of renewable resources and electricity demand, they often neglect the intertemporal dynamics of technological learning and the flexibility of power loads. This study builds upon existing research by developing a unified optimization framework to address these limitations in the context of China's unique energy system and geographical characteristics.

This study developed a spatially explicit optimization model to determine the optimal location, capacity, and construction timing of new utility-scale PV and wind power plants in China from 2021 to 2060. The model minimizes the levelized cost of electricity (LCOE) by considering various factors, including initial investment, operation and maintenance (O&M) costs, land acquisition, UHV transmission, energy storage, and power-load flexibility. The model identifies potential locations for 3844 new power plants (2767 PV, 1066 onshore wind, and 11 offshore wind) based on resource availability, administrative boundaries, land suitability, and environmental constraints. The model accounts for diurnal and seasonal variations in power generation and demand, allowing for the adjustment of hourly power demand to match supply (excluding essential uses like heating/cooling). It also incorporates the learning dynamics of renewable energy technologies, reducing LCOE by optimizing the construction timing of power plants. The model incorporates the expansion of UHV transmission capacity and the integration of energy storage to improve power-use efficiency. Sensitivity analyses were conducted to assess the impacts of various factors, including the capacity limit for individual plants, UHV transmission, energy storage, electrification rates, and power-load flexibility. Finally, the model assesses the cost of CO2 emissions reduction, and the co-benefits of poverty alleviation related to revenue generation and redistribution.

The optimized deployment strategy significantly increases the capacity of PV and wind power in China. The model projects a capacity increase from 9 PWh year⁻¹ (based on current projections) to 15 PWh year⁻¹ by 2060. This is achieved by optimizing the location, capacity, and construction timing of new power plants, coupled with significant investments in UHV transmission lines and energy storage. The average abatement cost is reduced from US$97 to US$6 per tonne of CO2. To achieve this, annual investment in PV and wind power needs to increase from US$77 billion in 2020 to US$127 billion in the 2020s and US$426 billion in the 2050s. The marginal abatement cost (MAC) varies from $166 per tCO2 to $106 per tCO2 in 2060 in the optimal path, with 77% of PV and wind power potentially competitive against nuclear power. The model shows that optimizing the construction time significantly reduces LCOE, and energy storage substantially increases CO2 emission reductions. The study also found that the expansion of PV and wind power requires a large land area (1,257,000 km²), distributed across deserts, grasslands, oceans, and cropland. The financial costs are substantial but are offset by savings from reduced fossil fuel purchases and carbon costs. Sensitivity analyses show that the results are robust, although factors such as discounting rates and plant lifetimes affect the cost estimates. The model also demonstrates that the redistribution of revenue from PV and wind power, particularly from developed eastern regions to less developed western regions, can contribute to poverty alleviation, lifting millions out of poverty and reducing income inequality.

This study's findings directly address the research question of how to optimize China's energy transition to achieve carbon neutrality by 2060. The results demonstrate the significant potential of PV and wind power in meeting this goal, but highlight the crucial role of infrastructure upgrades and policy interventions. The substantial investment requirements necessitate a strong policy push to encourage and facilitate large-scale renewable energy deployment. The findings underscore the importance of integrating spatially explicit modeling, power load flexibility, and technological learning dynamics for accurate and effective energy transition planning. The co-benefits related to poverty alleviation further emphasize the multifaceted advantages of accelerating renewable energy adoption in China.

This study provides a detailed roadmap for China's energy transition to carbon neutrality by 2060, showing that significant increases in PV and wind power are achievable with substantial investments and optimized infrastructure. The findings highlight the need for large-scale investment in power system upgrades, including UHV transmission lines and energy storage, to maximize the effectiveness and reduce the cost of renewable energy deployment. The co-benefits of poverty reduction from revenue redistribution in less developed regions further support the economic and social viability of this ambitious target. Future research should focus on refining cost estimations, further exploring the interactions between land use and energy production, and investigating policy mechanisms for effective implementation of the proposed plan.

The study's findings are based on a sophisticated model but still rely on certain assumptions and data inputs that may affect the accuracy of the projections. For example, the model's accuracy depends on the reliability of cost estimations for renewable energy technologies and related infrastructure, as well as projections of future electricity demand. Uncertainties in technological advancements and policy changes could also influence the actual outcomes. Furthermore, non-economic factors, such as social acceptance and political considerations, are not explicitly included in the model, but can significantly impact the implementation of the proposed strategy.