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Building carbon emission prediction plays an irreplaceable role in low-carbon economy development, public health protection and environmental sustainability. It is significant to identify influential factors mainly contributed to building emission and predict emission accurately in order to harness the growth from the source. In this paper, 11 influencing factors of building carbon emission are identified and a support vector regression (SVR) prediction model is proposed to forecast building carbon emission considering improvement the prediction accuracy, generalization, and robustness. In the SVR model, parameters are optimized by particle swarm optimization (PSO) algorithm with the aim to improve performance. Cases in Shanghai’s building sector are adopted to demonstrate practical applications of the proposed PSO-SVR prediction model. The results indicate that the presented prediction system has an outstanding performance in forecasting building carbon emission under multi-criteria evaluation. Furthermore, compared to the results from other four prediction models (e.g., linear regression, decision tree), it is shown that PSO-SVR model can achieve higher accuracy (e.g., improvement average of 1.01% R2 under training subset), better generalization (e.g., improvement average of 19.89% R2 under testing subset), and better robustness (e.g., improvement average of 18.93% R2 under different levels of noise intensity).

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Moral hazard have a non-negligible impact on supply chain sustainability, especially from a long-term perspective. This influence is more complicated in a dual-channel supply chain with free riding. Therefore, it is necessary to explore how manufacturers design multi-period incentive strategies in a dual-channel supply chain to deal with moral hazard problems from retailers. In this study, we built a game theory model that contains a retailer (she) who is delegated by a manufacturer (he) to sell products in her offline and online channels and to provide experience services in a physical store. The retailer has the option of exerting effort when providing experience services to boost demand. We explored and compared the manufacturer’s strategies that cover a time horizon of multiple periods under two circumstances: full information and repeated moral hazard. The following conclusions were drawn from this study. In the repeated moral hazard game, the incentive constraints of the retailer are only related to her current and the next-period profits and independent from the profits in other periods. Moreover, the incentive strategies in each period are affected by the historical information in the previous period, while the strategies under information symmetry are not affected by history. Specially, the manufacturer can induce effort by charging an up-front payment from the retailer in the previous period and then returning a utility based on the achieved demand. Therefore, the manufacturer can postpone the payment of incentive costs and shift the risk to the next period. Furthermore, the manufacturer’s incentive strategies are also affected by the free-riding effect between channels. That is, compared with the low-state transfer payment, the high-state transfer payment was found to be more sensitive to free riding.