Accurate carbon price index prediction can delve deeply into the internal law of carbon price changes, provide helpful information to managers and decision makers, as well as improve the carbon market system. Nevertheless, existing methods for combination forecasting typically arbitrarily choose a certain set of single forecasting models. However, a particular selection of forecasting models do not apply to all data sets due to the nonlinearity and nonsmoothness of the carbon trading price series. Therefore, choosing suitable single forecasting models for the combination is crucial. Considering the limitations of the current study, this study constructs a combined carbon trading forecasting model based on Lasso regression and optimal integration. By invoking the Lasso regression model, we can select suitable single forecasting models for combination forecasting based on the variation patterns of different training sets. Meanwhile, ARIMA, NARNN, LSTM, and 11 other single forecasting models are screened in this study, including both traditional statistical forecasting models and artificial intelligence forecasting models. First, the carbon price index is predicted using 11 single prediction models. Furthermore, given the multi-collinearity of the single prediction series, this study employs Lasso regression to reduce the dimensions of the single prediction models, which are then used to construct an optimal combination prediction model. Finally, the proposed model is applied to SZA-2017 and SZA-2019 carbon price data in Shenzhen. The results demonstrate that the model developed in this study outperforms other benchmark prediction models in terms of prediction error and direction accuracy, showing the efficacy of the proposed method.

China has been reforming the energy sector, especially the electricity industry. One of the important aims is to reform the cross‐subsidies (the industrial sector has been subsidizing the residential sector for decades), which requires policy‐makers have a good understanding of price and income elasticities of electricity. However, few studies estimated China’s price and income elasticities of electricity, partly due to limited micro data. This study estimates price and income elasticities of electricity in China using AIDS (Almost Ideal Demand System) demand model, which is consistent with consumer theory and more flexible than other regression models. The AIDS demand model is estimated using provincial‐level data from 2006 to 2016 and the estimation results show that the uncompensated price elasticity is ‐0.062, and the estimated income elasticity of residents is 0.889. Based on the estimated price elasticity of electricity, we simulate the consumer surplus under different pricing mechanisms. Ramsey pricing, as expected, generated the larger consumer surplus than actual pricing mechanism, which would provide guidance for the price reform of China's power industry.

Sustainable air-quality governance requires not only local emission reduction but also a shift from single-pollutant control to coordinated PM_2.5–O₃ control, and from city-bounded management to regional governance under spatial spillovers. Based on balanced annual city-level panel data for the “2 + 26” urban agglomeration in the Beijing–Tianjin–Hebei region and surrounding areas from 2013 to 2020, this paper uses the dynamic Spatial Durbin Model (SDM) to analyze the spatial spillover effect of PM_2.5 and O₃ pollution and the effect of regional governance policies. The results show that both PM_2.5 and O₃ exhibit significant spatial autocorrelation and cross-city dependence, indicating that isolated local control measures are insufficient for sustainable air pollution prevention and that city-bounded governance cannot fully address regionally connected pollution risks. Economic output and secondary-industry employment remain important structural factors of pollution. The policy-text analysis shows that measures centered on coal-related control and industrial governance were more directly aligned with PM_2.5 reduction, whereas O₃-related governance lagged, suggesting that single-pollutant-oriented control may generate a sustainability trade-off when PM_2.5 reduction is not accompanied by coordinated O₃ control. These findings highlight two sustainability challenges in China’s regional air-quality governance: first, single-pollutant control can improve particulate pollution but may not ensure sustainable air-quality improvement when O₃ and its precursors are insufficiently addressed; second, isolated city-level governance may be insufficient when pollution outcomes exhibit significant spatial dependence across administrative boundaries. The study provides empirical evidence for sustainable air-quality governance by emphasizing differentiated PM_2.5 and O₃ responses, coordinated PM_2.5–O₃ control, regional governance beyond individual city boundaries, and the integration of spatial spillover assessment into regional environmental policy design.

Promoting sustainable development requires a clear understanding of how short-term fluctuations in anthropogenic emissions affect urban environmental quality. This is especially relevant for cities experiencing rapid industrial changes or emergency policy interventions. Among key environmental concerns, variations in ambient pollutants like ozone (O₃) are closely tied to both public health and long-term sustainability goals. However, traditional chemical transport models often face challenges in accurately estimating emission changes and providing timely assessments. In contrast, statistical approaches such as the difference-in-differences (DID) model utilize observational data to improve evaluation accuracy and efficiency. This study leverages the synthetic difference-in-differences (SDID) approach, which integrates the strengths of both DID and the synthetic control method (SCM), to provide a more reliable and accurate analysis of the impacts of interventions on city-level air quality. Using Shanghai’s 2022 lockdown as a case study, we compare the deweathered ozone (O₃) concentration in Shanghai to a counterfactual constructed from a weighted average of cities in the Yangtze River Delta (YRD) that did not undergo lockdown. The quasi-natural experiment reveals an average increase of 4.4 μg/m³ (95% CI: 0.24–8.56) in Shanghai’s maximum daily 8 h O₃ concentration attributable to the lockdown. The SDID method reduces reliance on the parallel trends assumption and improves the estimate stability through unit- and time-specific weights. Multiple robustness checks confirm the reliability of these findings, underscoring the efficacy of the SDID approach in quantitatively evaluating the causal impact of emission perturbations on air quality. This study provides credible causal evidence of the environmental impact of short-term policy interventions, highlighting the utility of SDID in informing adaptive air quality management. The findings support the development of timely, evidence-based strategies for sustainable urban governance and environmental policy design.