Reply to the first systematic response by the Global Footprint Network to criticism: A real debate finally?

We respond to a reaction of the Global Footprint Network/GFN on our 8-point criticism of the ecological footprint. We also refer to, and comment on, an associated debate in this journal between Giampietro and Saltelli, 2014a, Giampietro and Saltelli, 2014b, on the one hand, and Goldfinger et al. (2014), on the other. We conclude that criticism on the footprint is accumulating and coherent across the various studies and disciplines and among the different authors. This was the first time that Wackernagel/GFN systematically responded to our criticisms. Hence, our response contains several original elements and the resulting exchange can be seen to add value to the existing literature. It ultimately allows readers to better make up their mind about the different viewpoints on the ecological footprint.     

Analysis of over-consumption of natural resources and the ecological trade deficit in China based on ecological footprints

China has experienced unprecedented economic development in recent years and is now facing severe challenges caused by the over-consumption of resources and by ecological and environmental degradation. To assess the influence of resource exploitation and ecological trade, we have developed an index of excessive resource consumption based on the concepts of ecological deficit and ecological over-shoot, and we have used the ecological trade deficit to assess the pressure created by the export and import of resources and products. Our analysis indicated that China's consumption footprint surpassed its biocapacity in 1983, leading to an ecological deficit, and the production footprint surpassed its biocapacity in 1986, leading to an ecological over-shoot, as the over-consumption of natural resources grew. By 2010, 3.6 times the current area of bioproductive land was needed to provide sufficient resources to meet the consumption. China has been encouraging the development of exporting enterprises by implementing a series of financial and tax incentives, which have stimulated the economy in the short-term but have gradually increased the ecological trade deficit since 2000.     

Supply versus use designs of environmental extensions in input–output analysis: Conceptual and empirical implications for the case of energy

Input–output analysis is one of the central methodological pillars of industrial ecology. However, the literature that discusses different structures of environmental extensions (EEs), that is, the scope of physical flows and their attribution to sectors in the monetary input–output table (MIOT), remains fragmented. This article investigates the conceptual and empirical implications of applying two different but frequently used designs of EEs, using the case of energy accounting, where one represents energy supply while the other energy use in the economy. We derive both extensions from an official energy supply–use dataset and apply them to the same single‐region input–output (SRIO) model of Austria, thereby isolating the effect that stems from the decision for the extension design. We also crosscheck the SRIO results with energy footprints from the global multi‐regional input–output (GMRIO) dataset EXIOBASE. Our results show that the ranking of footprints of final demand categories (e.g., household and export) is sensitive to the extension design and that product‐level results can vary by several orders of magnitude. The GMRIO‐based comparison further reveals that for a few countries the supply‐extension result can be twice the size of the use‐extension footprint (e.g., Australia and Norway). We propose a graph approach to provide a generalized framework to disclosing the design of EEs. We discuss the conceptual differences between the two extension designs by applying analogies to hybrid life‐cycle assessment and conclude that our findings are relevant for monitoring of energy efficiency and emission reduction targets and corporate footprint accounting.     

基于GTWR模型的浙江省海岸带三维生态足迹动态变化及其影响因素

厘清海岸带自然资本存量消耗和自然资本流量占用情况及其影响因素,能有效缓解海岸带社会经济发展与自然资源、生态环境之间的矛盾,促进区域协调可持续发展。本研究基于二维和三维生态足迹评价模型,分析浙江省海岸带2006—2016年生态足迹时空变化特征,基于足迹深度和足迹广度探究了区域自然资本存量和资本流量占用情况,并利用时空地理加权模型分析其影响因素的时空异质性。结果表明: 研究期间,浙江省海岸带生态危机加深,人均生态赤字平均值为3.5 hm²·cap^-1,人均生态赤字上升了15.5%,其中,能源消耗账户对人均生态足迹的贡献率大于生物资源账户;各县区人均生态足迹呈增长趋势,且空间分异显著。浙江省海岸带以消耗自然资本存量为主,且对自然资本流量更新起到较大的抑制作用;2016年,自然资本存量的消耗量是资本流量占有量的14.87倍。城市规模和经济发展水平、人民收入水平对浙江省海岸带各县区生态足迹以正向促进作用为主,渔业生产对生态足迹为负向影响,并有向正向影响作用转换的趋势,3个影响因子的影响机制时空分异性显著。     

土地流转背景下不同经营规模青田稻鱼共生系统的环境影响差异——基于碳足迹的实证研究

在快速工业化和城镇化的影响下,农业文化遗产的保护与管理正面临着适龄劳动力大量外流、土地抛荒、传统知识体系难以维持等诸多威胁与挑战。推动土地流转、进行适度规模经营,可在农业文化遗产的保护中产生积极作用。土地流转在给遗产地带来经济效益的同时,对当地生态环境产生的影响变化同样值得关注,但现有研究却少有涉及。本研究以全球重要农业文化遗产——浙江青田稻鱼共生系统为例,将不同经营规模的稻鱼共生系统分为小农户经营模式和规模化经营模式,运用生命周期法对两种模式的碳足迹进行核算。结果表明: 小农户经营模式和规模化经营模式的碳足迹分别为6510.80和5917.00 kg CO₂-eq·hm^-2,单位产值碳足迹分别为0.13和0.10 kg CO₂-eq·yuan^-1。与小农户经营模式相比,规模化经营模式温室气体排放更少,单位产值的环境影响更小。农户扩大经营规模后,当地温室气体排放减少了4097.20 kg CO₂-eq。农业生产过程中积累的CH₄在碳足迹中占比最大,农业生产资料中复合肥是仅次于CH₄的第二大温室气体排放来源。对于小农户经营模式,饲料中使用的玉米和小麦也对温室气体排放有重要的影响。因此,推动土地适度规模经营,有利于传统农业系统实现经济效益和环境效益的双赢,对于农业文化遗产保护具有重要作用。     

福建省灰水足迹时空变化及驱动因素

灰水足迹是稀释水污染物至达标需要的淡水体积,是评价水污染程度与水环境质量的重要方法,对灰水足迹进行核算与分析,可以促进福建省提高水环境质量,构建可持续的水生态环境。借鉴Hoekstra等提出的灰水足迹核算方法,对福建省及各地市2001-2017年的灰水足迹进行核算,对其时空变化特征进行评价并使用对数平均迪式指数分解法（Logarithmic Mean Divisia Index Method,LMDI）模型对灰水足迹变动的驱动因素进行分解。结果表明：a）总氮是决定灰水足迹总量的主要污染物,非点源污染是灰水足迹的最主要来源但占比由68.25%降至63.35%;b）福建省灰水足迹总量降低了9.58%,且各项指标都呈下降趋势,在空间上,灰水足迹总量及剩余灰水足迹东南多西北少,人均灰水足迹及灰水足迹强度东少西多;c）福建省灰水足迹变动的驱动因素中,经济因素是最大正向驱动因素,产生2932.96亿m³的贡献量,技术因素是最大负向驱动因素,产生-2630.31亿m³的贡献量。最后,针对福建省水污染问题提出建议：a）开展非点源污染防治专项;b）加快速度提高城镇生活污水处理水平;c）优化产业结构,提高灰水足迹效率;d）切实落实生态补偿制度,调动各市水环境保护积极性;e）加强环境监管力度,加大技术投入。     

南北疆区域经济差异化三维生态足迹自然资本利用的时空演变

如何刻画自然资源消费在区域间的流转机制及其对生态的影响,是测度区域可持续发展的难点。采用改进后的三维生态足迹模型并引入存量流量利用比和资本流量占用率,对2003—2018年新疆区域三维生态足迹进行核算,并分别测度新疆各地州市自然资本利用的程度,聚焦南北疆地区自然资本利用的差异性,利用偏最小二乘法(PLS)找出影响生态足迹变化的动因,最后结合基尼系数及差异系数量化南北疆经济差异程度,从自然和经济视角探究南北疆可持续发展态势与差异。结果表明:①2003—2018年新疆人均生态足迹持续增长,增长幅度为119.94%,人均生态承载力基本保持在1.7 hm~2左右,流量资本占用强度不断增加,生态赤字问题愈发严重。②研究期内,南北疆地区生态足迹的增长幅度和速度存在明显空间差异,呈现"北高快,南低慢"的态势。各地州市生态赤字情况呈现不同程度的增长趋势,高值区为北疆的克拉玛依市及天山北坡经济带。③第一产业增加值对生态足迹正向驱动作用最为显著,第三产业增加值对生态足迹起到逆向驱动作用。④2003—2018年间,南北疆地区基尼系数维持在0.4—0.5之间,处于中警警度水平,南北疆区域经济差异系数变化波动不大,北疆区域经济差异系数明显高于南疆区域。     

中国34个省级行政区三维生态足迹动态研究

当前中国正处于生态文明建设的关键时期,东西部环境资源不均衡的格局日益加剧,限制了区域社会经济的可持续发展。基于资源、社会经济统计数据,利用三维生态足迹模型对中国34个省级行政区进行动态研究,细化不同类型土地的均衡因子与产量因子,刻画2009—2016年人均生态足迹广度、生态足迹深度的时空特征,并探讨分析省域间的资本利用格局及其社会经济影响因素。主要结论如下：人均生态足迹广度在省域间差异较大,且生态足迹深度均已超过自然原长1.00;足迹广度在研究期间呈先降后升趋势,最高值为西藏的10.87—12.35 hm2/cap,最小值为澳门的0.02 hm2/cap;且资本流量占用率并不充足,广西与上海分别仅为47.68%—67.34%、66.31%—68.88%。香港、澳门、宁夏的足迹深度远高于中国均值,分别为19.24—26.02、8.60—10.88、4.60—7.46,最小值为西藏（1.64—1.79）,其中耕地、化石能源用地的生态赤字贡献率最高,而林地则为生态盈余状态。将各省自然资本利用格局聚类分为4类并通过ArcGIS进行空间分析。基于改进的三维生态足迹模型并系统性地应用于中国34个省域,本研究定量化各省的自然资本利用格局,可为中国区域间资源配置、政策制定提供基础数据支撑及环境决策参考。     

Assessing the Environmental Impact of Water Consumption by Energy Crops Grown in Spain

The environmental impact of the water consumption of four typical crop rotations grown in Spain, including energy crops, was analyzed and compared against Spanish agricultural and natural reference situations. The life cycle assessment (LCA) methodology was used for the assessment of the potential environmental impact of blue water (withdrawal from water bodies) and green water (uptake of soil moisture) consumption. The latter has so far been disregarded in LCA. To account for green water, two approaches have been applied: the first accounts for the difference in green water demand of the crops and a reference situation. The second is a green water scarcity index, which measures the fraction of the soil‐water plant consumption to the available green water. Our results show that, if the aim is to minimize the environmental impacts of water consumption, the energy crop rotations assessed in this study were most suitable in basins in the northeast of Spain. In contrast, the energy crops grown in basins in the southeast of Spain were associated with the greatest environmental impacts. Further research into the integration of quantitative green water assessment in LCA is crucial in studies of systems with a high dependence on green water resources.     

On the importance of taking into account agricultural practices when defining conservation priorities for regional planning

Conserving biodiversity in managed landscapes requires the definition of spatial conservation priorities. The systematic conservation planning tools which are used to define these conservation priorities, assess the vulnerability of different locations by combining two different elements: some measurement of the biological assets in question, and some measurement of the key processes which threaten these biological assets. For instance, in cumulative impact mapping, maps of individual human activities that impact ecosystems (hereafter referred to as ‘stressor’ for individual maps and ‘cumulative stressor’ for combined maps) are overlaid with maps of ecosystem vulnerability, in order to estimate the overall ecological impact of human activities on natural ecosystems. These tools are appealing because they are easy to use and inform regional land planning. However, given that once these spatial conservation priorities are defined they potentially have far-reaching consequences, there is a need to test their robustness and reliability. Here we propose to investigate how the uncertainties related to the estimation of a cumulative stressor layer affect the definition of spatial conservation priorities. We conduct a sensitivity analysis of the different ways of estimating major stressors related to human activities (transport, urbanization and population) with a specific focus on agriculture. We show that spatial conservation priorities are little sensitive to most of the parameters and input data used to estimate the cumulative stressor map. In particular, they are not very sensitive to changes in spatially overlapping stressors, i.e. those which overlap spatially with other stressors. However, our analyses also reveal that spatial conservation priorities are highly sensitive to how the agriculture stressor is defined. These results highlight the importance of better understanding how agricultural activities impact biodiversity and establishing how more accurate information on agricultural practices can be used to define spatial conservation priorities.