The climate factors are the main driving forces of variability of renewable energy sources. In facing the safety challenges of the large-scale grid integration of renewable energy, it is important to understand the effects of climate shocks on the generation of the renewables as well as if the complementarity among different types of renewable energy exist and could be employed to smooth out the production and better match with the load. We employ a Bayesian vector autoregressive (BVAR) model along with impulse response function and forecast error variance decomposition to investigate the dynamic complementarity among renewable energy in the face of different climate shocks. The proposed methodology is applied over Qinghai employing the power generation data as well as meteorological data. The results show that there is greater general complementarity between solar power and hydropower. Wind power and solar power, and wind power and hydropower, exhibit dynamic complementarity when there are fluctuations in precipitation and wind speed, respectively. China should make efficient use of the natural complementarity advantages between renewable energy sources, carry out joint optimal dispatching, and accelerate the construction of a clean, low-carbon, safe and efficient energy system.

This paper develops a dynamic game-theoretic model to evaluate market competitiveness in industries characterized by price competition and adjustment stickiness. We extend the dynamic oligopoly framework for estimating market competitiveness in the literature from a quantity-setting to a price-setting context with differentiated goods. By deriving the subgame perfect equilibrium in a linear-quadratic structure, we utilize an index analogous to the price conjectural variation to measure market competitiveness with differentiated goods. The model is applied to the Chinese retail oil market, and we find that the Chinese retail oil market, particularly dominated by two state firms, exhibits characteristics close to a collusive benchmark within the maintained model. The dynamic game model provides a tractable analytical tool for antitrust authorities to monitor strategic coordination in dynamic environments where price transparency or regulation may facilitate tacit coordination of pricing behavior to a high degree.

Composite environmental risks accumulate unevenly along urban–rural gradients, yet the conditional and nonlinear interaction between built environment quality (BEQ) and urban–rural functional connectivity (URFC) remains poorly quantified at fine resolution. This study aims to determine whether, and under what conditions, BEQ moderates the relationship between URFC and a population-weighted composite risk index (CRI), and to translate the result into spatia targeted green-infrastructure priorities. We use 744,714 grid cells at 100 m resolution over Guangzhou, China. The framework couples entropy-weighted BEQ from satellite and street-view imagery, gravity-model URFC computed on the real road network, and a two-stage population-weighted CRI of heat and air hazards. We apply nested ordinary least squares with incremental F-tests, spatial-lag and spatial-error models, generalised additive models with B-spline bases, gradient-boosted trees with SHAP interaction values, and Baron–Kenny mediation analysis. The main BEQ × URFC estimates are negative across the parametric and machine-learning specifications. The interaction is, however, small: a spatial-lag model on a 10,000-cell subsample returns β = −5.4 × 10⁻⁴, but a scalable generalised-method-of-moments spatial regression on the full grid—where the spatial autoregressive coefficient reaches ρ ≈ 0.99—shows the coefficient to be negative yet not statistically significant, and a five-seed re-estimation confirms that the subsample-based significance is draw-dependent. We therefore interpret the buffering as directionally supported but small and not robustly significant once spatial autocorrelation is fully modelled. The buffering response is nonlinear in the GAM main effects, and BEQ buffers across the entire observed connectivity range rather than switching sign at an interior threshold; URFC functions predominantly as a moderator rather than a mediator. Population-stratified estimation shows that the buffering is exposure-conditional: it is strongest where population exposure is high and weakens or reverses in sparsely populated cells, consistent with the risk = hazard × exposure structure of CRI. Sensitivity tests across values of the distance-decay parameter, 100 entropy perturbations and spatial scales corroborate the buffering direction. The framework provides an evidentiary basis for prioritising green infrastructure in functionally connected, populated but environmentally degraded transition zones.

This paper proposes a framework for designing an emissions trading system (ETS) for the highly concentrated and regulated Chinese electricity forward markets. We present an economic model where a few generation firms maximize profits by choosing output levels under a price set below equilibrium and engage in trading carbon permits under either tradable performance standards (TPS) or cap-and-trade (C&T) systems. The regulated price causes underproduction across all firms. Compared to a scenario with no ETS, both ETS depress permit buyers' production, and C&T typically decreases sellers' production unless initial allocations are excessively high. However, TPS encourages permit sellers' production if a seller cannot drastically shift the permit market equilibrium. From a Guangdong dataset, we uncover model parameters, such as firm-specific cost functions, and use them to evaluate policy scenarios. We find TPS achieves higher welfare with lower emissions than C&T under the regulated price. If the price is jointly selected with the ETS to maximize welfare, both achieve almost identical welfare and emission levels, but TPS has more consumer surplus than C&T. Moreover, TPS under the regulated price approximates this maximized welfare, counteracting the underproduction by reallocating generation to low-emission-intensity firms, whereas C&T requires substantially higher power and permit prices.