基于二次能源省际调配的中国分省CO2排放量计算

节能减排目标任务的制定需要依据科学合理的CO2排放量测算。现有的IPCC提供的CO2排放量计算方法仅考虑一次能源燃料所产生的CO2,未考虑到二次能源省际调配的情况,不能真实反映各省CO2排放情况。本研究提出了考虑二次能源省际调配情况下CO2排放量的计算方法,并以2009年的数据为例,对各省能源消费CO2排放量进行了计算。考虑二次能源省际调配后,传统的能源大省如内蒙古、山西的CO2排放总量下降,东部沿海省份的CO2排放总量上升。中西部地区的CO2排放强度仍显著高于东部地区。中西部地区存在能源利用效率低、能源加工技术设备落后的情况,导致了西部地区的CO2排放强度偏高。建议中央在实施西部大开发"十二五"规划时,应当加强对中西部地区能源加工行业的投资,改善能源加工技术,改良加工设备,提高能源加工效率,降低CO2排放强度。建议由能源调入省向能源调出省份实施补偿。该部分补偿资金用于调出省的能源产业升级改造,以顺利实现节能减排的目标。     

Key factors that influence households’ tree planting behaviour

Despite a decrease in indigenous forests and a growing demand for tree products in developing countries, tree planting activities are not considerably expanding in Tanzania. In this paper, we analyse factors that influence households’ tree planting behaviour, as well as the number of trees planted. Coast and Morogoro regions in the east of Tanzania were selected as the case, and data was gathered from 202 households in 11 villages in these regions where tree planting programmes have been or still are active. A Heckman model is used to analyse the factors that drive tree planting behaviour. Results indicate that households get wood energy from forest reserves (57%), in addition to their own planted trees (9.1%). Emperical findings show that the most important factors have significantly positive effects on households’ tree planting behaviour, as well as the extent to which it was implemented. These factors include households’ land sizes, households’ awareness of tree planting programmes, tree planting for wood energy, and the age of the head of the household. The right/freedom to harvest and transport tree products, households’ attitudes towards tree planting, and family size have significantly negative effects on households’ tree planting behaviour. This paper is perhaps the first comprehensive study to analyse the factors that influence households’ tree planting behaviour in Tanzania, and it uncovers results that are useful, even for other developing countries with similar conditions.     

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

Economic dispatch of power system containing wind power and photovoltaic considering carbon trading and spare capacity variation

The drying up of the fossil energy sources and the damage from unchecked carbon emissions demand the development of low carbon economy, which promotes the development of new energy sources, such as wind power and photovoltaic. However, the direct connections of wind/photovoltaic power into power grid bring great impacts on power systems, thus affecting the security and stability of power system operations, which challenges the power system dispatching. In despite of many methods for power system dispatch, lack of the models, for power system containing wind power and photovoltaic considering carbon trading and spare capacity variation (PSCWPCCTSCV), restricts the further optimal operations of power systems. This paper studies the economic dispatch modeling problem of power system containing wind power and photovoltaic, establishes the model of economic dispatch of PSCWPCCTSCV. On this basis, adaptive immune genetic algorithm is applied to conduct the economic operation optimization, which can provide the optimal carbon trading price and the optimal power distribution coefficient. Finally, simulations based on the newly proposed models are made to illustrate the economic dispatch of PSCWPCCTSCV. The results show that optimization with the proposed model can not only weaken the volatility of the new energy effectively, but also reduce carbon emissions and reduce power generation costs.     

The effect of industry activities on public support for ‘fracking’

Research suggests that previous, current, and prospective extractive industry activities influence perceptions of new development. Studies that have drawn this conclusion, however, have usually focused on specific projects in specific communities. Here, these factors are examined on an aggregate, national scale. Combining geospatial data on extractive industry activities and survey data from a nationally representative sample (N = 1061), the influence of extractive industry activities on support for fracking is studied. While limited evidence is found for the impact of proximity to oil and gas wells or production on support for fracking, employment levels in the natural resources and mining sector in the respondent’s county and residence in an area experiencing active oil and gas development significantly increase support for fracking. The results highlight the role of spatial and community factors in shaping support for energy development.     

基于LCA的废旧资源循环利用节能减排效果评估模式与方法研究——以吉林省某钢铁企业为例

促进废旧资源循环利用是加快推进我国生态文明建设,完成节能减排目标的必然选择。本文基于生命周期评价模式,从微观企业层面入手,构建产品全生命周期基准流程,引入能量输入与环境输出参数,建立废旧资源循环利用节能减排效果量化核算模型,评估再生产品的节能减排经济成效,并以吉林省某钢铁企业为例,评估"废钢-电炉"短流程和"铁矿石-高炉-转炉"长流程的能源、环境、成本差异,辨识影响废钢再循环节能减排效果的主要因素和重要环节。结果显示,再生钢铁全生命周期与原生钢铁全生命周期相比,节能588.48kgce/t,节能率为84%;主要污染物中SO2减排率最高,达92%;CO2总减排1 180.92 kg/t,减排率为67%;总成本却高出198元/t。其中,炼铁工序的节能量和减碳量最大,烧结工序SO2、NOx和烟(粉)尘减排量最大,焦化工序COD和氨氮减排量最大,回收、加工处理、炼钢环节节能量和减碳量以及SO2、NOx和烟(粉)尘减排量均为负。成本方面,再生钢铁生产成本高于原生钢铁308元/t,虽然再生钢铁由于污染减排可节省56元/t的排污费并获取54元/t的碳交易收益,但都不足以扭转电炉炼钢费用较高的现状。因此,国家应在电炉炼钢方面给予钢企及相关企业适当的财税扶持政策,在电价方面给予钢企一定的优惠或补贴,并完善废钢回收加工体系等,以促进废钢循环利用。基于LCA的废旧资源循环利用节能减排效果评估可以实现对产品生命周期全过程的资源、环境、成本的优化管理。     

城镇化与工业化对能源强度影响的实证研究——基于截面相关和异质性回归系数的非平衡面板数据模型

城镇化、工业化对中国能源强度的影响如何?如何在快速推进城镇化、工业化进程的同时确保节能减排目标的实现?论文以能源强度指标代替传统的能源消费指标来反映能源综合利用效率,并应用考虑截面相关性和异质性回归系数的非平衡面板数据模型,使用共同相关效应组均值(CCEMG)估计方法对中国1978-2014年城镇化、工业化与能源强度之间的关系进行分析。研究结果表明:人均实际GDP增长1%,能源强度将会降低0.412%,工业化水平增长1%,能源强度将会上升0.630%,而由于生产消费等经济活动的增加、高度集中化以及规模经济的综合作用,使得城镇化对能源强度的影响并不确定。联系研究结论,本文提出政策建议:我国应加快产业结构升级,转变经济增长方式;构建绿色制造体系,推进"五化"协同发展;推进绿色、循环、低碳发展的新型城镇化建设,提高城镇化质量,提升我国整体的能源效率,确保节能减排目标的实现,推动经济全面、协调、可持续发展。     

Rebound effect and its decomposition – an analysis based on energy types in China

Rebound effect derived from energy efficiency improvement has been widely invested. However, most of studies focus on the rebound effect of the energy composite level and neither distinguish nor compare different energy types. We compare the differences in energy saving and energy rebound between primary and secondary energy sources, and further decompose the rebound effect into production rebound part and final demand component. To do so, we add a module for rebound into a comparative state China-CGE model. We design and test two simulation scenarios using the model. In Scenario 1, all production sectors’ energy efficiency of using primary energy increases by 5%. In Scenario 2, all production sectors’ energy efficiency of using secondary energy increases by 5%. The results show that Scenario 2 leads to more GDP growth and more energy saving. Our scenarios show rebound effects range between 9.6% and 27.9%, and in general are higher when energy efficiency of using primary energy sources is improved. Our decomposition analysis shows that improving energy efficiency in production sectors would stimulates energy use of final demand. Indeed, the consumption side has significant contribution to rebound in secondary energy use, especially in crude oil and gas. This study reveals that improving efficiency of using secondary energy is better than improving that of primary energy, both in terms of economic impact and energy rebound. And complementary policies that prevent energy services prices from falling too much can be adopted to reduce rebound. Controlling residential energy use could also be effective in reducing rebound, this has particular implication to economies in which residential energy consumption are far from saturation.     

Global trends and development of energy services industry in China

Energy service is an effective way to promote energy conservation by market mechanisms, including energy saving services, energy procurement, supply of many varieties of energy, supply of renewable energy technologies, energy-related consulting services, risk management, etc. China is a major energy consumer but energy is in short supply, and the efficiency of energy use is low. China’s energy service industry has expanded rapidly, in terms of both the number of new Energy Service Companies entering the market and amount of capital invested in Energy Performance Contracting projects, but the energy service sector in China is still at an early stage of development. Developed countries began early in developing the energy service sector and their energy service market is mature, and the experience of developed countries shows that energy services play a significant role in advancing energy saving and emission reduction. Under the new situation, China needs combine energy services experience of developed countries, and take following measures to accelerate China’s energy services rapid and healthy development, including the long-term aspects of policy planning, energy-saving core technology, finance and capital investment, public sector reductions, personnel training, and so on.     

The influence of Chinese population policy change on resources and the environment

Universal two-child policy has been implemented since the end of 2015 in China. This policy is anticipated to bring a significant increase in the total population, with profound influences on the resources and environment in the future. This paper analyzes the changing dynamics of urban and rural population, and forecasts urban and rural population from 2016 to 2030 at national and provincial scale using a double log linear regression model. Drawing upon the results of these two predictions, the impact of the population policy change on Chinese resources consumption and environmental pollution are predicted quantitatively. Given the future total population maintains current levels on resources consumption and environmental emission, the additional demand of resources and environment demand for the new population is forecasted and compared against the capacity on supply side. The findings are as follows: after implementing the universal two-child policy, China’s grain, energy consumption, domestic water demand, and pollutant emissions are projected to increase at different rates across provinces. To meet the needs arising from future population growth, food and energy self-sufficiency rate will be significantly reduced in the future, while relying more on imports. Stability of the water supply needs to be improved, especially in Beijing, Henan, Jiangsu, Qinghai, and Sichuan where the gap in future domestic water demand is comparatively larger. Environmental protection and associated governing capability are in urgent need of upgrade not least due to the increasing pressure of pollution.