Ecological footprint and real income: Panel data evidence from the 27 highest emitting countries

This study examines the effects of real income, financial development and trade openness on the ecological footprint (EF) of consumption using a panel data of leading world EF contributors during the period 1991–2012. A number of panel unit root tests confirm that the data are first-difference stationary. Results from Pedroni co-integration tests show that the variables are co-integrated. The panel dynamic ordinary least squares (DOLS) method is then employed to estimate the long run association between the variables. The results indicate a positive and significant association between ecological footprint (EF) and real income, and a negative and insignificant impact of trade openness on EF. Financial development is also observed to reduce EF. Afterwards, the group-mean fully modified ordinary least squares method is applied to check the robustness of the DOLS estimates. The findings are partially robust as only real income confirms the positive significant impact on EF. In addition, the vector error correction model supports a unidirectional causal impact running from real income to EF. Finally, findings from variance decomposition analysis and impulse response functions reveal that real income will have an increasing effect on EF for the selected countries into the future.     

Integrating ecological and water footprint accounting in a multi-regional input–output framework

Carbon, ecological, and water footprints (CF, EF, and WF) are accounting tools that can be used to understand the connection between consumption activities and environmental pressures on the Earth's atmosphere, bioproductive areas, and freshwater resources. These indicators have been gaining acceptance from researchers and policymakers but are not harmonized with one another, and ecological and water footprints are lacking in their representation of product supply chains. In this paper we integrate existing methods for calculating EF and WF within a multi-regional input–output (MRIO) modelling framework that has already been successfully applied for CF estimation. We introduce a new MRIO method for conserving the high degree of product detail found in existing physical EF and WF accounts. Calculating EF and WF in this way is consistent with the current best practice for CF accounting, making results more reliable and easier to compare across the three indicators. We discuss alternatives for linking the MRIO model and the footprint datasets and the implications for results. The model presented here is novel and offers significant improvements in EF and WF accounting through harmonization of methods with CF accounting, preservation of product-level detail, comprehensive inclusion of sectors of the global economy, and clear representation of flows along supply chains and international trade linkages. The matrix organization of the model improves transparency and provides a structure upon which further improvements in footprint calculation can be built. The model described here is the first environmentally extended MRIO model that harmonizes EF and WF accounts and aligns physical unit data of product use with standard economic and environmental accounting.     

Evaluating and presenting uncertainty in model‐based unconstrained ordination

Variability in ecological community composition is often analyzed by recording the presence or abundance of taxa in sample units, calculating a symmetric matrix of pairwise distances or dissimilarities among sample units and then mapping the resulting matrix to a low‐dimensional representation through methods collectively called ordination. Unconstrained ordination only uses taxon composition data, without any environmental or experimental covariates, to infer latent compositional gradients associated with the sampling units. Commonly, such distance‐based methods have been used for ordination, but recently there has been a shift toward model‐based approaches. Model‐based unconstrained ordinations are commonly formulated using a Bayesian latent factor model that permits uncertainty assessment for parameters, including the latent factors that correspond to gradients in community composition. While model‐based methods have the additional benefit of addressing uncertainty in the estimated gradients, typically the current practice is to report point estimates without summarizing uncertainty. To demonstrate the uncertainty present in model‐based unconstrained ordination, the well‐known spider and dune data sets were analyzed and shown to have large uncertainty in the ordination projections. Hence to understand the factors that contribute to the uncertainty, simulation studies were conducted to assess the impact of additional sampling units or species to help inform future ordination studies that seek to minimize variability in the latent factors. Accurate reporting of uncertainty is an important part of transparency in the scientific process; thus, a model‐based approach that accounts for uncertainty is valuable. An R package, UncertainOrd , contains visualization tools that accurately represent estimates of the gradients in community composition in the presence of uncertainty.     

Product Carbon Footprints and Their Uncertainties in Comparative Decision Contexts

In response to growing awareness of climate change, requests to establish product carbon footprints have been increasing. Product carbon footprints are life cycle assessments restricted to just one impact category, global warming. Product carbon footprint studies generate life cycle inventory results, listing the environmental emissions of greenhouse gases from a product’s lifecycle, and characterize these by their global warming potentials, producing product carbon footprints that are commonly communicated as point values. In the present research we show that the uncertainties surrounding these point values necessitate more sophisticated ways of communicating product carbon footprints, using different sizes of catfish (Pangasius spp.) farms in Vietnam as a case study. As most product carbon footprint studies only have a comparative meaning, we used dependent sampling to produce relative results in order to increase the power for identifying environmentally superior products. We therefore argue that product carbon footprints, supported by quantitative uncertainty estimates, should be used to test hypotheses, rather than to provide point value estimates or plain confidence intervals of products’ environmental performance.     

生态足迹的模型修正与方法改进

生态足迹是测定人类活动的资源消费需求,判明自然资产是否被过度利用的有效工具。介绍了生态足迹的基本概念和模型,简单分析基本模型存在的主要缺陷和争论,重点解析了近年来生态足迹模型在参数调整、项目计算、账户扩展等方面的演变和修正。介绍生态足迹研究的传统方法:综合法和组分法,评述了生命周期评价,基于投入产出分析,三维模型,净初级生产力,能值理论,时序分析等的方法改进。对未来的研究方向提出自己的看法,期望对我国的生态足迹研究有一定的启示作用。     

Accounting for control mislabeling in case-control biomarker studies

In biomarker discovery studies, uncertainty associated with case and control labels is often overlooked. By omitting to take into account label uncertainty, model parameters and the predictive risk can become biased, sometimes severely. The most common situation is when the control set contains an unknown number of undiagnosed, or future, cases. This has a marked impact in situations where the model needs to be well-calibrated, e.g., when the prediction performance of a biomarker panel is evaluated. Failing to account for class label uncertainty may lead to underestimation of classification performance and bias in parameter estimates. This can further impact on meta-analysis for combining evidence from multiple studies. Using a simulation study, we outline how conventional statistical models can be modified to address class label uncertainty leading to well-calibrated prediction performance estimates and reduced bias in meta-analysis. We focus on the problem of mislabeled control subjects in case-control studies, i.e., when some of the control subjects are undiagnosed cases, although the procedures we report are generic. The uncertainty in control status is a particular situation common in biomarker discovery studies in the context of genomic and molecular epidemiology, where control subjects are commonly sampled from the general population with an established expected disease incidence rate.     

中国1999年生态足迹计算与发展能力分析

可持续发展的定量评估是可持续发展研究的关键领域,其核心是确定人类的生存是否处于生态系统的承载力范围之内．新近提出和发展起来的生态足迹指标是一种测算人类对自然利用程度的新的综合指标,该方法通过将区域的资源和能源消费转化为提供这种物质流所必需的各种生物生产土地的面积(生态足迹),并同区域能提供的生物生产土地面积(生态承载力)进行比较,能定量判断一个区域的发展是否处于生态承载力的范围内．以中国和部分省(区市）1999年的统计数据为基础,对中国和部分省(区市)1999年的生态足迹计算结果表明,1999年中国的人均生态足迹为1．326hm^2,而人均生态承载力为0．681hm^2,人均生态赤字为0、645hm^2;分省的计算结果也表明大部分省(区市)的生态足迹超过了当地的生态承载力．生态赤字的存在表明,区域的经济社会发展处于一种不可持续的发展状态．同时,将生态足迹计算中得到的不同土地类型面积作为测算生态经济系统多样性的指标,测算了中国及部分省(区市)1999年生态足迹的多样性,并采用Ulanowicz的发展能力公式分析了各省的发展能力．发展能力是一个较好的预测产出的指标,增加多样性是增加发展能力的有效途径、另外,还分析了生态足迹的多样性与资源利用效益的关系,讨论了生态足迹及其多样性的政策含义．     

生态补偿对大熊猫栖息地周边农户生态足迹的影响

大熊猫栖息地正面临破碎化和人类活动干扰的威胁,如何减少这些威胁成为保护大熊猫这一生态旗舰物种的关键.通过实施生态补偿项目,充分考虑当地弱势群体生存和发展的权利,帮助其进行生计替代和能源替代,以减小生态系统所承受的压力.以120个项目农户作为研究对象,利用生态足迹方法,探讨了生态补偿项目对生活在关键生态区域内农村人口的影响.结果表明,在各种生态生产性土地中,农户对林地和草地生态占用最大,能源替代可以大幅降低农户的薪柴消耗水平,从而降低农户对林地的生态占用.通过调整农户的牲畜养殖模式,减小了放牧对草地资源的压力和对草地的生态占用.农户人均生态足迹由2005年的2.7346hm2减小至2007年的1.6325 hm2,对周边环境的压力降低,有利于保护和恢复珍稀野生动植物栖息地.     

Ecological Footprint Analysis Applied to Mobile Phones

Ecological footprints (EF) have been used for more than 15 yr as an aggregate measure of sustainability of geographical regions, but also for certain products and activities. EF analysis measures the bioproductive areas required to produce resources such as crops and timber, the directly occupied areas for infrastructure, and areas for absorbing waste flows (mostly limited to carbon dioxide) in a given year for a defined population. The need to extend ecological footprint analysis to electronic products arose because so far, mobile phones have mainly been evaluated using life-cycle assessments with a focus on toxicity, end-of-life management, and energy use, thus ignoring the wider sustainability implications. This article presents the footprint results from three mobile phone case studies. To establish the land areas consumed by the mined materials used in electronic products, a database was developed based on literature data and on approximations from the density and overburden of materials. The relationship between abundance and overburden values was used in a regression analysis to estimate energy requirements in materials extraction where other data were not available. Using a life-cycle assessment approach, environmental burdens for producing and using a mobile phone were calculated and transformed into the instantaneous rate of resource consumption. Key results were that different electronic products have different ecological footprints and that the methodology proved sensitive enough to reveal differences in small electronic products and for monitoring technologies that use bioproductive space efficiently.     

Accounting for Age Uncertainty in Growth Modeling,the Case Study of Yellowfin Tuna (Thunnus albacares) of the Indian Ocean

Age estimates, typically determined by counting periodic growth increments in calcified structures of vertebrates, are the basis of population dynamics models used for managing exploited or threatened species. In fisheries research, the use of otolith growth rings as an indicator of fish age has increased considerably in recent decades. However, otolith readings include various sources of uncertainty. Current ageing methods, which converts an average count of rings into age, only provide periodic age estimates in which the range of uncertainty is fully ignored. In this study, we describe a hierarchical model for estimating individual ages from repeated otolith readings. The model was developed within a Bayesian framework to explicitly represent the sources of uncertainty associated with age estimation, to allow for individual variations and to include knowledge on parameters from expertise. The performance of the proposed model was examined through simulations, and then it was coupled to a two-stanza somatic growth model to evaluate the impact of the age estimation method on the age composition of commercial fisheries catches. We illustrate our approach using the saggital otoliths of yellowfin tuna of the Indian Ocean collected through large-scale mark-recapture experiments. The simulation performance suggested that the ageing error model was able to estimate the ageing biases and provide accurate age estimates, regardless of the age of the fish. Coupled with the growth model, this approach appeared suitable for modeling the growth of Indian Ocean yellowfin and is consistent with findings of previous studies. The simulations showed that the choice of the ageing method can strongly affect growth estimates with subsequent implications for age-structured data used as inputs for population models. Finally, our modeling approach revealed particularly useful to reflect uncertainty around age estimates into the process of growth estimation and it can be applied to any study relying on age estimation.     

Global net biome CO2 exchange predicted comparably well using parameter–environment relationships and plant functional types

Accurate estimation and forecasts of net biome CO₂ exchange (NBE) are vital for understanding the role of terrestrial ecosystems in a changing climate. Prior efforts to improve NBE predictions have predominantly focused on increasing models' structural realism (and thus complexity), but parametric error and uncertainty are also key determinants of model skill. Here, we investigate how different parameterization assumptions propagate into NBE prediction errors across the globe, pitting the traditional plant functional type (PFT)-based approach against a novel top-down, machine learning-based “environmental filtering” (EF) approach. To do so, we simulate these contrasting methods for parameter assignment within a flexible model–data fusion framework of the terrestrial carbon cycle (CARDAMOM) at a global scale. In the PFT-based approach, model parameters from a small number of select locations are applied uniformly within regions sharing similar land cover characteristics. In the EF-based approach, a pixel's parameters are predicted based on underlying relationships with climate, soil, and canopy properties. To isolate the role of parametric from structural uncertainty in our analysis, we benchmark the resulting PFT-based and EF-based NBE predictions with estimates from CARDAMOM's Bayesian optimization approach (whereby “true” parameters consistent with a suite of data constraints are retrieved on a pixel-by-pixel basis). When considering the mean absolute error of NBE predictions across time, we find that the EF-based approach matches or outperforms the PFT-based approach at 55% of pixels—a narrow majority. However, NBE estimates from the EF-based approach are susceptible to compensation between errors in component flux predictions and predicted parameters can align poorly with the assumed “true” values. Overall, though, the EF-based approach is comparable to conventional approaches and merits further investigation to better understand and resolve these limitations. This work provides insight into the relationship between terrestrial biosphere model performance and parametric uncertainty, informing efforts to improve model parameterization via PFT-free and trait-based approaches.     

Combined data, Bayesian phylogenetics, and the origin of the New Zealand cicada genera

We have applied Bayesian and maximum likelihood methods of phylogenetic estimation to data from four mitochondrial genes (COI, COII, 12S, and 16S) and a single nuclear gene (EF1alpha) from several genera of New Zealand, Australian, and New Caledonian cicada taxa. We specifically focused on the heterogeneity of phylogenetic signal among the different data partitions and the biogeographic origins of the New Zealand cicada fauna. The Bayesian analyses circumvent many of the problems associated with other statistical tests for comparing data partitions. We took an information-theoretic approach to model selection based on the Akaike Information Criterion (AIC). This approach indicated that there was considerable uncertainty in identifying the best-fit model for some of the partitions. Additionally, a large amount of uncertainty was associated with many parameter estimates from the substitution model. However, a sensitivity analysis on the combined dataset indicated that the model selection uncertainty had little effect on estimates of topology because these estimates were largely insensitive to changes in the assumed model. This outcome suggests strong signal in our data. Our analyses support a New Caledonian affiliation of the New Zealand cicada genera Maoricicada, Kikihia, and Rhodopsalta and Australian affinities for the genera Amphipsalta and Notopsalta. This result was surprising, given that previous cicada biologists suspected a close relationship between Amphipsalta, Notopsalta, and Rhodopsalta based on genitalic characters. Relationships among the closely related genera Maoricicada, Kikihia, and Rhodopsalta were poorly resolved, the mitochondrial data and the EF1alpha data favoring different arrangements within this clade.     

Importance of dose‐response model form in probabilistic risk assessment: A case study of health effects from methylmercury in fish

We compared the effect of uncertainty in dose‐response model form on health risk estimates to the effect of uncertainty and variability in exposure. We used three different dose‐response models to characterize neurological effects in children exposed in utero to methylmercury, and applied these models to calculate risks to a native population exposed to potentially contaminated fish from a reservoir in British Columbia. Uncertainty in model form was explicitly incorporated into the risk estimates. The selection of dose‐response model strongly influenced both mean risk estimates and distributions of risk, and had a much greater impact than altering exposure distributions. We conclude that incorporating uncertainty in dose‐response model form is at least as important as accounting for variability and uncertainty in exposure parameters in probabilistic risk assessment.     

Urban total ecological footprint forecasting by using radial basis function neural network: A case study of Wuhan city,China

Ecological footprint (EF) forecasting is essential for dynamically evaluating human impact on earth as well as for planning for a sustainable future. In this paper, a radial basis function neural network (RBFNN) model was developed to forecast the total ecological footprint (TEF) from 2006 to 2015. For a case study of Wuhan city, Hubei province in central China, per capita ecological footprint (EF) and biological capacity (BC) were calculated from 1988 to 2005. Partial least square (PLS) was used to select the important impact factors. We put the selected socio-economic factors as input and the TEF as output together to build RBFNN model and predict the development trends of the TEF in the following 10 years. Five-fold cross-validation was conducted to validate the model in the process of input selection and RBFNN model training. From the results, continuous increase of per capita EF (1988–2005) indicated stronger and stronger human effect on the district and Wuhan's ecological state is in the ecological deficit. Up to 2015, the district would have been bearing accumulative TEF of 24.782 million gha, which is near 2.5 times of that in 1988. Although the increase rate of gross domestic product (GDP) would be restricted in a lower level from 2006 to 2015, the urban ecological environmental burden could only respond to the socio-economic circumstances moderately.     

A method to incorporate the effect of taxonomic uncertainty on multivariate analyses of ecological data

Researchers in ecology commonly use multivariate analyses (e.g. redundancy analysis, canonical correspondence analysis, Mantel correlation, multivariate analysis of variance) to interpret patterns in biological data and relate these patterns to environmental predictors. There has been, however, little recognition of the errors associated with biological data and the influence that these may have on predictions derived from ecological hypotheses. We present a permutational method that assesses the effects of taxonomic uncertainty on the multivariate analyses typically used in the analysis of ecological data. The procedure is based on iterative randomizations that randomly re‐assign non identified species in each site to any of the other species found in the remaining sites. After each re‐assignment of species identities, the multivariate method at stake is run and a parameter of interest is calculated. Consequently, one can estimate a range of plausible values for the parameter of interest under different scenarios of re‐assigned species identities. We demonstrate the use of our approach in the calculation of two parameters with an example involving tropical tree species from western Amazonia: 1) the Mantel correlation between compositional similarity and environmental distances between pairs of sites, and; 2) the variance explained by environmental predictors in redundancy analysis (RDA). We also investigated the effects of increasing taxonomic uncertainty (i.e. number of unidentified species), and the taxonomic resolution at which morphospecies are determined (genus‐resolution, family‐resolution, or fully undetermined species) on the uncertainty range of these parameters. To achieve this, we performed simulations on a tree dataset from southern Mexico by randomly selecting a portion of the species contained in the dataset and classifying them as unidentified at each level of decreasing taxonomic resolution. An analysis of covariance showed that both taxonomic uncertainty and resolution significantly influence the uncertainty range of the resulting parameters. Increasing taxonomic uncertainty expands our uncertainty of the parameters estimated both in the Mantel test and RDA. The effects of increasing taxonomic resolution, however, are not as evident. The method presented in this study improves the traditional approaches to study compositional change in ecological communities by accounting for some of the uncertainty inherent to biological data. We hope that this approach can be routinely used to estimate any parameter of interest obtained from compositional data tables when faced with taxonomic uncertainty.     

A historical meta‐analysis of global terrestrial net primary productivity: are estimates converging?

Net primary productivity (NPP) is one of the most important ecosystem parameters, representing vegetation activity, biogeochemical cycling, and ecosystem services. To assess how well the scientific community understands the biospheric function, a historical meta‐analysis was conducted. By surveying the literature from 1862 to 2011, I extracted 251 estimates of total terrestrial NPP at the present time (NPP_T) and calculated their statistical metrics. For all the data, the mean±standard deviation and median were 56.2±14.3 and 56.4 Pg C yr^–1, respectively. Even for estimates published after 2000, a substantial level of uncertainty (coefficient of variation by ±15%) was inevitable. The estimates were categorized on the basis of methodology (i.e., inventory analysis, empirical model, biogeochemical model, dynamic global vegetation model, and remote sensing) to examine the consistency among the statistical metrics of each category. Chronological analysis revealed that the present NPP_T estimates were directed by extensive field surveys in the 1960s and 1970s (e.g., the International Biological Programme). A wide range of uncertainty remains in modern estimates based on advanced biogeochemical and dynamic vegetation models and remote‐sensing techniques. Several critical factors accounting for the estimation uncertainty are discussed. Ancillary analyses were performed to derive additional ecological and human‐related parameters related to NPP. For example, interannual variability, carbon‐use efficiency (a ratio of NPP to gross photosynthesis), human appropriation, and preindustrial NPP_T were assessed. Finally, I discuss the importance of improving NPP_T estimates in the context of current global change studies and integrated carbon cycle research.     

Determinants of Ecological Footprints: What is the role of globalization?

This paper empirically analyzes the ecological consequences of globalization, by employing the Ecological Footprint (EF) as a proxy for human ecological demands and the KOF index of Globalization. We develop an unbalanced data set covering 146 countries over the 1981–2009 period and are thus able to address the influence of countries’ development over time. After empirically showing that globalization is an explanatory factor of ecological demands, an Extreme Bounds Analysis (EBA) identifies a robust set of impact factors. Subsequently, specific hypotheses on economic, political, social and overall globalization guide the empirical analysis. The findings suggest that economic globalization drives the EF of consumption, production, imports and exports. Social globalization correlates negatively with the EF of consumption and production, while increasing the EF of imports and exports. No effects are found for political globalization while overall globalization is positively correlated with EFs of imports and exports. The findings show that globalization may have different effects on EFs depending on the dimension (consumption, production, exports and imports) referred to.     

表达生态承载力的生态足迹模型演变

生态足迹是一种表达区域生态承载力的重要指标．其基本模型具有方法简单、综合、可比、结果表达生动等优点,但在预测性、结构性、用户适用性等方面均存在一定的缺陷．为了克服这些缺陷,人们从不同方面对模型进行了补充与完善,推动了生态足迹研究由最初的单一时间尺度静态模型向多样化发展与演变．截止目前,主要生态足迹模型包括：时间序列足迹模型、投入产出法足迹模型、综合评价法足迹模型、土地干扰度足迹模型、生命周期法足迹模型（或组分法足迹模型）．这些模型不同程度地改进了生态足迹作为生态承载力表达指标的性能,但生态足迹核算的准确性和完整性仍有待进一步完善．     

Estimation of the carbon dioxide (CO2) fertilization effect using growth rate anomalies of CO2 and crop yields since 1961

The effect of elevated carbon dioxide (CO₂) on crop yields is one of the most uncertain and influential parameters in models used to assess climate change impacts and adaptations. A primary reason for this uncertainty is the limited availability of experimental data on CO₂ responses for crops grown under typical field conditions. However, because of historical variations in CO₂, each year farmers throughout the world perform uncontrolled yield ‘experiments’ under different levels of CO₂. In this study, measurements of atmospheric CO₂ growth rates and crop yields for individual countries since 1961 were compared to empirically determine the average effect of a 1 ppm increase of CO₂ on yields of rice, wheat, and maize. Because the gradual increase in CO₂ is highly correlated with major changes in technology, management, and other yield controlling factors, we focused on first differences of CO₂ and yield time series. Estimates of CO₂ responses obtained from this approach were highly uncertain, reflecting the relatively small importance of year‐to‐year CO₂ changes for yield variability. Combining estimates from the top 20 countries for each crop resulted in estimates with substantially less uncertainty than from any individual country. The results indicate that while current datasets cannot reliably constrain estimates beyond previous experimental studies, an empirical approach supported by large amounts of data may provide a potentially valuable and independent assessment of this critical model parameter. For example, analysis of reliable yield records from hundreds of individual, independent locations (as opposed to national scale yield records with poorly defined errors) may result in empirical estimates with useful levels of uncertainty to complement estimates from experimental studies.