Waste-to-energy (WtE) incineration is a feasible way to respond to both the municipal solid waste management and renewable energy challenges, but few studies have been carried out on its environmental and economic impact in fast-developing southeastern Asian countries. To fill such a research gap, this study innovatively conducted a holistic assessment of WtE incineration application potential in Java Island, Indonesia. Here, we have established a life cycle assessment model for WtE incineration in Java, and have estimated the environmental impact, electricity generation potential, and techno-economic feasibility of implementing incineration by 2025. We have revealed that global warming potential, terrestrial ecotoxicity potential, eutrophication potential, and acidification potential are the major environmental impacts stemming from incineration activities. Moreover, we have estimated that promoting incineration in Java could reduce CO₂ emissions by 41% on average. The electricity generated from incineration could contribute to 3.72% of Indonesia’s renewable energy target for the electricity grid mix by 2025. The cumulative energy production potential from incineration is estimated to reach 2,316,523 MWh/year in 2025 and will increase by 14.3% in 2050. The techno-economic assessment of incineration implementation in Java cities has been enumerated as feasible. The levelized cost of electricity from incineration (0.044 USD/kWh) is competitive with the current Indonesian electricity price (0.069 USD/kWh). Policies of minimizing incineration pollution, providing financial support guarantees, and overcoming social barriers have been proposed to facilitate the application of WtE incineration.

Accurate long-term power load forecasting in the grid is crucial for supply–demand balance analysis in new power systems. It helps to identify potential power market risks and uncertainties in advance, thereby enhancing the stability and efficiency of power systems. Given the temporal and nonlinear features of power load, this paper proposes a hybrid load-forecasting model using attention mechanisms, CNN, and BiLSTM. Historical load data are processed via CEEMDAN, K-means clustering, and VMD for significant regularity and uncertainty feature extraction. The CNN layer extracts features from climate and date inputs, while BiLSTM captures short- and long-term dependencies from both forward and backward directions. Attention mechanisms enhance key information. This approach is applied for seasonal load forecasting. Several comparative experiments show the proposed model’s high accuracy, with MAPE values of 1.41%, 1.25%, 1.08% and 1.67% for the four seasons. It outperforms other methods, with improvements of 0.25–2.53 GWh² in MSE, 0.15–0.1 GWh in RMSE, 0.1–0.74 GWh in MAE and 0.22–1.40% in MAPE. Furthermore, the effectiveness of the data processing method and the impact of training data volume on forecasting accuracy are analyzed. The results indicate that decomposing and clustering historical load data, along with large-scale data training, can both boost forecasting accuracy.

In order to mitigate global warming and improve air quality, the transformation of regional energy structures is the most important development pathway. China, as a major global consumer of fossil fuels, will face great pressure in this regard. Aiming toward achieving the global 2 °C warming target in China, this study takes one of the most developed regions of China, Guangdong Province, as the research area in order to explore a future development pathway and potential air quality attainment until 2050, by developing two energy structure scenarios (BAU_Energy and 2Deg_Energy) and three end-of-pipe scenarios (NFC, CLE, and MTFR), and simulating future air quality and related health impacts for the different scenarios using the WRF-Chem model. The results show that under the energy transformation scenario, total energy consumption in Guangdong rises from 296 Mtce (million tons of coal equivalent) in 2015 to 329 Mtce in 2050, with electricity and clean energy accounting for 45% and 35%. In 2050, the transformation of the energy structure leads to 64%, 75%, and 46% reductions in the emissions of CO₂, NOx, and SO₂ compared with those in 2015. Together with the most stringent end-of-pipe control measures, the emissions of VOCs and primary PM_2.5 are effectively reduced by 66% and 78%. The annual average PM_2.5 and MDA8 (daily maximum 8 h O₃) concentrations in Guangdong are 33.8 and 85.9 μg/m³ in 2015, with 63.4 thousand premature deaths (95% CI: 57.1–70.8) due to environmental exposure. Under the baseline scenario, no improvement is gained in air quality or public health by 2050. In contrast, the PM_2.5 and MDA8 concentrations decline to 21.7 and 75.5 μg/m³ under the scenario with energy structure transformation, and total premature deaths are reduced to 35.5 thousand (31.9–39.5). When further combined with the most stringent end-of-pipe control measures, the PM_2.5 concentrations decrease to 16.5 μg/m³, but there is no significant improvement for ozone, with premature deaths declining to 20.6 thousand (18.5–23.0). This study demonstrates that the transformation of energy structure toward climate goals could be effective in mitigating air pollution in Guangdong and would bring significant health benefits. Compared with the end-of-pipe control policies, transformation of the energy structure is a more effective way to improve regional air quality in the long term, and synergistic promotion of both is crucial for regional development.

In recent years, due to the rise in energy prices and the impact of COVID-19, energy shortages have led to unsafe power supply environments. High emissions industries which account for more than 58% of the carbon emissions of Guangdong Province have played an important role in achieving the carbon peak goal, alleviating social energy shortage and promoting economic growth. Controlling high emissions industries will help to adjust the industrial structure and increase renewable energy investment. Therefore, it is necessary to comprehensively evaluate the policies of energy security and the investments of high emission industries. This paper builds the ICEEH-GD (comprehensive assessment model of climate, economy, environment and health of Guangdong Province) model, designs the Energy Security scenario (ES), the Restrict High Carbon Emission Sector scenario (RHS) and the Comprehensive Policy scenario (CP), and studies the impact of limiting high emissions industries and renewable energy policies on the transformation of investment structure, macro-economy and society. The results show that under the Energy Security scenario (ES), carbon emissions will peak in 2029, with a peak of 681 million tons. Under the condition of ensuring energy security, the installed capacity of coal-fired power generation will remain unchanged from 2025 to 2035. Under the Restrict High Carbon Emission Sector scenario (RHS), the GDP will increase by 8 billion yuan compared with the ES scenario by 2035. At the same time, it can promote the whole society to increase 10,500 employment opportunities, and more investment will flow to the low emissions industries. In the Comprehensive Policy scenario (CP), although the GDP loss will reach 33 billion yuan by 2035 compared with the Energy Security scenario (ES), the transportation and service industries will participate in carbon trading by optimizing the distribution of carbon restrictions in the whole society, which will reduce the carbon cost of the whole society by more than 48%, and promote the employment growth of 104,000 people through industrial structure optimization. Therefore, the power sector should increase investment in renewable energy to ensure energy security, limit the new production capacity of high emissions industries such as cement, steel and ceramics, and increase the green transition and efficiency improvement of existing high emissions industries.

This study proposes four kinds of hybrid source–grid–storage systems consisting of photovoltaic and wind energy, and a power grid including different batteries and hydrogen storage systems for Sanjiao town. HOMER-PRO was applied for the optimal design and techno-economic analysis of each case, aiming to explore reproducible energy supply solutions for China’s industrial clusters. The results show that the proposed system is a fully feasible and reliable solution for industry-based towns, like Sanjiao, in their pursuit of carbon neutrality. In addition, the source-side price sensitivity analysis found that the hydrogen storage solution was cost-competitive only when the capital costs on the storage and source sides were reduced by about 70%. However, the hydrogen storage system had the lowest carbon emissions, about 14% lower than the battery ones. It was also found that power generation cost reduction had a more prominent effect on the whole system’s NPC and LCOE reduction. This suggests that policy support needs to continue to push for generation-side innovation and scaling up, while research on different energy storage types should be encouraged to serve the needs of different source–grid–load–storage systems.