基于边际治理成本的环境保护税税率标准探究与政策建议

税负水平是税收调节作用的重要因素,各地区需要科学地制定符合本地区发展水平的环境保护税税率来平衡环境保护与经济发展.基于2016年东北某省14个市的钢铁、电力、化工、水泥行业的排污及治理成本数据,建立污染削减费用函数模型,计算该省大气、水中主要污染物的边际治理成本,并设置不同环境保护税税率的情景方案,以探究环境保护税税率对该省经济发展的影响.结果表明:①SO₂、NO_x、COD、NH₃-N的边际治理成本分别为3.44、5.03、3.81、8.15元/污染当量. ②重点行业主要污染物的边际治理成本不同,钢铁行业主要污染物的边际治理成本较高,其COD、NH₃-N的边际治理成本均高于总体边际治理成本;化工行业的COD边际治理成本高于总体边际治理成本;电力行业的大气主要污染物边际治理成本高于总体边际治理成本;水泥行业的大气主要污染物边际治理成本则相对较低. ③适当提高环境保护税税率对经济发展总体影响不大.研究显示:该省的边际治理成本远高于目前的环境保护税征收标准,若以边际治理成本作为环境保护税税率,不但会增加企业负担,而且会导致不同行业的边际治理成本相差较大,因此,建议提高该省的环境保护税税率标准,即大气污染物征收标准为2.4元/污染当量,水污染物征收标准为2.8元/污染当量,并设置环境保护税税率的行业差异化,将有利于政策功能的发挥.      

经济发展成本分析:可持续发展经济学的理论基础

可持续发展战略存在着理论与实践上的严重反差，其主要原因是可持续发展经济缺乏统一的理论基础，本文通过对经济学不同流派关于可持续发展问题研究的评析，认为可持续发展经济学需要建立一个统一的理论基础，并提出经济发展成本分析是可持续发展经济学的理论基础，围绕这一理论基础对可持续发展的概念进行了重新界定，并主张要把制度的激励与约束以及政策的引导纳入可持续发展经济途径研究的视野之中。     

基于成本收益分析的油气管道风险控制决策过程

为实现对油气管道风险的有效控制,结合油气管道的特点,基于成本收益分析方法改进风险控制决策过程。首先,确定风险控制目标,对所建议的措施进行初步筛选,并根据行业现状确定基线场景;之后,量化筛选后的措施的成本与收益,通过计算成本收益率(CBR)或规避事故隐含成本(ICAF)来确定相应措施的合理性;最后,依据上述分析制定风险控制决策。结果表明:不同措施的CBR或ICAF值不同,CBR或ICAF值较低的措施应被优先实施;而当CBR或ICAF值超过标准值时,相应措施是不合理的,应不予以实施。     

Cumulative carbon emissions and economic policy: In search of general principles

We exploit recent advances in climate science to develop a physically consistent, yet surprisingly simple, model of climate policy. It seems that key economic models have greatly overestimated the delay between carbon emissions and warming, and ignored the saturation of carbon sinks that takes place when the atmospheric concentration of carbon dioxide rises. This has important implications for climate policy. If carbon emissions are abated, damages are avoided almost immediately. Therefore it is optimal to reduce emissions significantly in the near term and bring about a slow transition to optimal peak warming, even if optimal steady-state/peak warming is high. The optimal carbon price should start relatively high and grow relatively fast.     

Spatial cost–benefit analysis of blue restoration and factors driving net benefits globally

Marine coastal ecosystems, commonly referred to as blue ecosystems, provide valuable services to society but are under increasing threat worldwide due to a variety of drivers, including eutrophication, development, land-use change, land reclamation, and climate change. Ecological restoration is sometimes necessary to facilitate recovery in coastal ecosystems. Blue restoration (i.e., in marine coastal systems) is a developing field, and projects to date have been small scale and expensive, leading to the perception that restoration may not be economically viable. We conducted a global cost–benefit analysis to determine the net benefits of restoring coral reef, mangrove, saltmarsh, and seagrass ecosystems, where the benefit is defined as the monetary value of ecosystem services. We estimated costs from published restoration case studies and used an adjusted-value-transfer method to assign benefit values to these case studies. Benefit values were estimated as the monetary value provided by ecosystem services of the restored habitats. Benefits outweighed costs (i.e., there were positive net benefits) for restoration of all blue ecosystems. Mean benefit:cost ratios for ecosystem restoration were eight to 10 times higher than prior studies of coral reef and seagrass restoration, most likely due to the more recent lower cost estimates we used. Among ecosystems, saltmarsh had the greatest net benefits followed by mangrove; coral reef and seagrass ecosystems had lower net benefits. In general, restoration in nations with middle incomes had higher (eight times higher in coral reefs and 40 times higher in mangroves) net benefits than those with high incomes. Within an ecosystem type, net benefit varied with restoration technique (coral reef and saltmarsh), ecosystem service produced (mangrove and saltmarsh), and project duration (seagrass). These results challenge the perceptions of the low economic viability of blue restoration and should encourage further targeted investment in this field.     

Monitoring,imperfect detection,and risk optimization of a Tasmanian devil insurance population

Most species are imperfectly detected during biological surveys, which creates uncertainty around their abundance or presence at a given location. Decision makers managing threatened or pest species are regularly faced with this uncertainty. Wildlife diseases can drive species to extinction; thus, managing species with disease is an important part of conservation. Devil facial tumor disease (DFTD) is one such disease that led to the listing of the Tasmanian devil (Sarcophilus harrisii) as endangered. Managers aim to maintain devils in the wild by establishing disease‐free insurance populations at isolated sites. Often a resident DFTD‐affected population must first be removed. In a successful collaboration between decision scientists and wildlife managers, we used an accessible population model to inform monitoring decisions and facilitate the establishment of an insurance population of devils on Forestier Peninsula. We used a Bayesian catch‐effort model to estimate population size of a diseased population from removal and camera trap data. We also analyzed the costs and benefits of declaring the area disease‐free prior to reintroduction and establishment of a healthy insurance population. After the monitoring session in May–June 2015, the probability that all devils had been successfully removed was close to 1, even when we accounted for a possible introduction of a devil to the site. Given this high probability and the baseline cost of declaring population absence prematurely, we found it was not cost‐effective to carry out any additional monitoring before introducing the insurance population. Considering these results within the broader context of Tasmanian devil management, managers ultimately decided to implement an additional monitoring session before the introduction. This was a conservative decision that accounted for uncertainty in model estimates and for the broader nonmonetary costs of mistakenly declaring the area disease‐free.     

Benefits of integrating complementarity into priority threat management

Conservation decision tools based on cost‐effectiveness analysis are used to assess threat management strategies for improving species persistence. These approaches rank alternative strategies by their benefit to cost ratio but may fail to identify the optimal sets of strategies to implement under limited budgets because they do not account for redundancies. We devised a multiobjective optimization approach in which the complementarity principle is applied to identify the sets of threat management strategies that protect the most species for any budget. We used our approach to prioritize threat management strategies for 53 species of conservation concern in the Pilbara, Australia. We followed a structured elicitation approach to collect information on the benefits and costs of implementing 17 different conservation strategies during a 3‐day workshop with 49 stakeholders and experts in the biodiversity, conservation, and management of the Pilbara. We compared the performance of our complementarity priority threat management approach with a current cost‐effectiveness ranking approach. A complementary set of 3 strategies: domestic herbivore management, fire management and research, and sanctuaries provided all species with >50% chance of persistence for $4.7 million/year over 20 years. Achieving the same result cost almost twice as much ($9.71 million/year) when strategies were selected by their cost‐effectiveness ranks alone. Our results show that complementarity of management benefits has the potential to double the impact of priority threat management approaches.     

边境贸易发展的环境影响预测及其调控研究——以满洲里市为例

边镜贸易作为我国作为开放的重要组成部分，进展显著。本文在概述我国边境贸易发展所产生的环境问题的基础上，以我国最大的陆边口岸－－满州里市为例，运用灰色预测，动态规划等方法探讨边境贸易与环境保护协调发展的途径。     

环境成本核算研究的进展

环境成本核算作为环境管理的基础工作，在绿色国民经济核算体系构建，在涉及环境费用效益分析，在制定适当的环境标准、环境收费等政策时发挥着重要作用。在陈述环境成本的定义和分类以及国内外环境成本核算研究过程的基础上，指出可持续发展理论、总成本理论、外部性和边际机会成本理论以及基于SEEA核算体系的环境成本计量理论是环境成本核算的四大理论支柱，并对环境成本核算方法的研究进行了总结与分析。阐明了研究队伍综合化、宏观核算微观化、核算方法科学化是环境成本核算研究的发展趋势，时空范围的处理、环境服务产出的处理、内部环保外部化处理是环境成本核算研究的难点与热点问题。     

River water quality management model using genetic algorithm

Conventional mathematical programming methods, such as linear programming, non linear programming, dynamic programming and integer programming have been used to solve the cost optimization problem for regional wastewater treatment systems. In this study, a river water quality management model was developed through the integration of a genetic algorithm (GA). This model was applied to a river system contaminated by three determined discharge sources to achieve the water quality goals and wastewater treatment cost optimization in the river basin. The genetic algorithm solution, described the treatment plant efficiency, such that the cost of wastewater treatment for the entire river basin is minimized while the water quality constraints in each reach are satisfied. This study showed that genetic algorithm can be applied for river water quality modeling studies as an alternative to the present methods.