The Relative Importance of Landscape Amenity and Health Impacts in the Wind Farm Debate in Australia

In recent years, Australia has experienced public debate around the growth in wind farms as part of government and community strategies to move from fossil fuels to renewable energy. Many of the arguments put forward by opponents and supporters are similar to those that have been evident in North America and Western Europe, including possible environmental impacts on wildlife, noise levels and, perhaps most prominently adverse changes in ‘landscape amenity’. A concern of increasing prominence is that of adverse human health impacts from proximity to operating turbines. Using analysis of submissions to public inquiries and a small number of detailed interviews, we consider the increased focus in Australia on the health impacts of wind farms. We note that health impacts, as elsewhere in the world, are often part of a suite of objections to wind farm developments but conclude that for some people near such developments, these are the main concern and not a cover or proxy for other concerns, such as changes in landscape amenity and aesthetics.     

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.     

Modeling urban solar energy with high spatiotemporal resolution: A case study in Toronto,Canada

This research presents a method to determine the maximum potential for the capturing of solar radiation on the rooftop of buildings in an urban environment. This involves the modeling of solar energy potential and comparison to historical building energy demand profiles through the use of 3-D solar simulation software tools and geographic information systems (GIS). The objective is to accurately identify the amount of surface area that is suitable for solar photovoltaic (PV) installations and to estimate the hourly PV electricity generation potential of existing building rooftops in an urban environment. This study demonstrates a viable approach for modeling urban solar energy and offers valuable information for electricity distributors, policy makers, and urban energy planners to facilitate the substantial design of a green built environment. The developed methodology is comprised of three main sections: (1) determination of suitable rooftop area, (2) determination of the amount of incident solar radiation available per rooftop, and (3) estimation of hourly solar PV electricity generation potential. A case study was performed using this method for Ryerson University, located in Toronto, Canada. It was found that solar PV could supply up to 19% of the study area’s electricity demands during peak consumption hours. The potential benefits of solar PV was also estimated based upon hourly greenhouse gas emission intensity factors as well as Time-of-Use (TOU) savings through the Ontario Feed-in-Tariff (FIT) program, which allows for better representation of the positive impacts of solar technologies.     

Moving targets: Carbon pricing,energy markets,and social movements in Australia

Reporting on the origins and directions of social movement strategy on climate and energy issues in the last decade, the shifts in ‘climate movement’ practice are discussed using a neo-Polanyian account of the political economy of climate change combined with sociological analysis of the strategic decisions campaigners reported making. Since the mid-2000s, Australia’s climate movement has been engaged in three concurrent arenas of political contestation. The longest-standing arena of movement activity has been negotiations over climate policy. More recently, activists and communities are engaged in a struggle over the expansion of fossil fuels. A third contest has been waged over the present and future position of renewable energy technologies in Australia’s electricity market. In the wake of climate policy failure, energy campaigns have been deepened, and it seems that a broader energy justice agenda is being forged. New strategic dilemmas are visible in the field.     

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。     

环境约束下大都市城郊土地利用结构评价——基于信息熵和非期望产出模型分析

土地利用过程中会产生期望和非期望产出,针对以往土地利用结构评价中忽略环境产生的非期望产出而影响土地利用结构效率真实性问题,以大都市城郊的上海青浦区为例,宏观上,运用信息熵模型对该区2005~2014年土地利用结构有序性进行评价;微观上,运用非期望产出模型测算青浦区研究期间土地利用结构效率,为寻求区域土地利用结构效率损失的原因及改善途径提供参考。结果表明:(1)2005~2014年青浦区土地利用结构信息熵值时序上呈现"M"形变化趋势,空间上由中部向东西两翼递减;(2)2005~2014年青浦区土地利用结构效率变化趋势在时间序列上呈现"W"形变化趋势,空间上由中部的中心城区向东西两翼递减格局;(3)将环境的负产出纳入非期望产出模型进行土地利用结构效率评价,能更加准确的反映土地利用结构效率的真实性;(4)影响青浦区土地利用结构效率水平变化的主要因素为纯技术效率的变化,资源过度消耗和环境污染物排放过量是制约青浦区土地利用结构效率提升的主要原因。针对土地利用结构效率损失的原因及区域差异提出效率提升途径。研究结果可为青浦区土地利用结构优化和可持续发展提供科学借鉴。     

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。     

家庭部门生活能源消费碳排放:测度与驱动因素研究

本文运用IPCC的二氧化碳排放量测算方法,在省际层面测度了我国家庭部门直接能源消费碳排放,并基于扩展的STIRPAT和Kaya模型,构建家庭部门直接能源消费碳排放影响因子动态面板数据模型,对我国2003-2012年分省面板数据样本及城乡子样本进行系统GMM估计。本文研究表明,第一,我国城乡家庭部门碳排放总量和人均碳排放在近十年都呈快速上升趋势,家庭部门碳排放的空间分布具有明显的地域差异特征,高碳排放地区主要集中在东、中部地区,西部地区的碳排放水平较低;第二,城乡家庭部门生活能源消费的上期碳排放量对本期碳排放产生重要的正向影响,这反映出我国家庭部门碳排放具有显著的惯性特征和路径依赖性,是一种动态自适应机制;人口规模、居民消费水平、能源消费结构、碳排放强度、能源消费强度和城镇化因素,都对我国居民能源消费碳排放总量及人均碳排放具有显著的影响,城乡之间的家庭能源消费碳排放驱动因素存在差异。本文研究得到如下启示及政策含义:家庭部门碳减排将是一个有步骤、分区域的渐进过程,我国碳减排政策应当兼顾消费升级和碳排放的双重目标,努力构建分层次碳减排的适应性预期机制。具体而言,一方面应着眼于引导和激励居民低碳消费,缓解人口规模增加、消费水平提升和城镇化对家庭部门碳减排的压力;另一方面要通过能源价格改革、财政政策和环境规制政策等优化我国能源消费结构,不断降低煤炭消费比重,提高清洁能源的消费。同时,通过技术创新、设备改造等科技手段提高煤炭利用效率,降低碳排放强度,这些政策将更有利于城镇家庭部门碳减排。     

基于SD和CLUE-S模型的区域土地利用变化对土壤有机碳储量影响研究

土地利用/土地覆被变化改变土壤呼吸条件,进而对土壤有机碳储量变化产生影响,而土壤有机碳储量则是影响农业可持续发展和全球碳平衡领域的重要因素。以上海市崇明岛为例,运用系统动力学模型(System Dynamics Model)预测2020、2030年土地利用需求变化,结合CLUE-S模型(Conversion of Land Use and its Effects at Small region extent Model)得出各种用地类型的空间分布,并引用碳密度法估算三种发展幕景下土地利用变化对土壤有机碳储量的影响。结果表明:2030年三种发展幕景土壤有机碳储量分别为:低速发展幕景为3 093.03×106kg,惯性发展幕景为3 079.47×106kg,高速发展幕景为3 059.81×106kg;研究期内土壤有机碳储量呈现缓慢下降趋势,但人类活动对其扰动较小;SD和CLUE-S耦合模型可以从时间和空间两方面对土壤有机碳储量进行模拟,具有可行性;建议通过加强城镇用地集约利用、农田保护、林地建设来减少人为活动对土壤有机碳储量的影响。