区域间供给驱动的碳排放溢出与反馈效应

减缓区域间的碳排放溢出-反馈效应对中国协调区域发展和实现绿色发展意义重大。本文基于供给多区域投入产出模型建立了供给视角下三区域碳排放溢出-反馈效应的分析方法,继而测算了2002-2010年中国东、中、西部之间的碳排放溢出-反馈乘数和实际效应。研究发现,在整个研究时期内东、中部对西部以及东、中部之间的溢出乘数有所下降,但西部对东、中部的溢出乘数有所上升。同时,三大地区的反馈乘数则都有所上升。不过,由于各个地区的经济总量都显著上升,因而各区域各种实际的碳排放效应在整个研究时期内都显著上升。东部对中、西部地区的溢出乘数明显小于中、西部对东部地区的溢出乘数,且东部地区的反馈乘数也几乎一直小于中部和西部。不过,由于东部地区的经济总量远远大于中、西部,因而东部地区各种实际的碳排放效应在整个研究时期内都显著大于中、西部地区相应的实际碳排放效应。三大地区碳排放溢出及反馈乘数效应较大的部门很相似,它们都以采掘业及相关下游重工业为主,如煤炭开采和洗选业。碳排放溢出及反馈乘数效应较小的部门则主要包括通信设备、计算机及其他电子设备制造业、建筑业以及服务业。为减缓区域间供给驱动的碳排放溢出-反馈效应,地区间应当加强在碳减排领域的合作,以此提升区域间供应链的碳排放效率;促进节能减排技术的推广应用以提高各部门特别是碳排放密集型产业的能源利用效率;同时应大力发展清洁型战略新兴产业并严格控制碳密集型产业的扩张,从而优化产业结构。     

A dynamic simulation model to support reduction in illegal trade within legal wildlife markets

Sustainable wildlife trade is critical for biodiversity conservation, livelihoods, and food security. Regulatory frameworks are needed to secure these diverse benefits of sustainable wildlife trade. However, regulations limiting trade can backfire, sparking illegal trade if demand is not met by legal trade alone. Assessing how regulations affect wildlife market participants’ incentives is key to controlling illegal trade. Although much research has assessed how incentives at both the harvester and consumer ends of markets are affected by regulations, little has been done to understand the incentives of traders (i.e., intermediaries). We built a dynamic simulation model to support reduction in illegal wildlife trade within legal markets by focusing on incentives traders face to trade legal or illegal products. We used an Approximate Bayesian Computation approach to infer illegal trading dynamics and parameters that might be unknown (e.g., price of illegal products). We showcased the utility of the approach with a small-scale fishery case study in Chile, where we disentangled within-year dynamics of legal and illegal trading and found that the majority (∼77%) of traded fish is illegal. We utilized the model to assess the effect of policy interventions to improve the fishery's sustainability and explore the trade-offs between ecological, economic, and social goals. Scenario simulations showed that even significant increases (over 200%) in parameters proxying for policy interventions enabled only moderate improvements in ecological and social sustainability of the fishery at substantial economic cost. These results expose how unbalanced trader incentives are toward trading illegal over legal products in this fishery. Our model provides a novel tool for promoting sustainable wildlife trade in data-limited settings, which explicitly considers traders as critical players in wildlife markets. Sustainable wildlife trade requires incentivizing legal over illegal wildlife trade and consideration of the social, ecological, and economic impacts of interventions.