Footprint Model Research Articles

Determining the optimal biomass-gasification-based fuel cell trigeneration system in exergy-based cost and carbon footprint method considering energy level

The accompanying exergy-cost-carbon nexus in trigeneration systems brings challenges to distinguishing the formation mechanisms of costs and carbon footprints of power, cold, and heat. In this paper, a fuel cell trigeneration system based on biomass gasification is constructed, and seven optional system configurations are designed according to the waste heat utilization methods including organic Rankine cycle, absorption cycle, and heat exchange. A novel exergy-based cost and carbon footprint model based on the energy levels of energy streams is proposed to obtain the allocation rules of multiple products of the trigeneration system, in which carbon cost with exergy cost is integrated into the exergo-economic method. Using this method, the coupling relationships of exergy-cost-carbon are illustrated and the unit exergy-carbon costs and carbon footprints of electricity, cold, and heat are determined. To select the optimum configuration, a multi-criteria decision-making method, considering energetic, economic, and environmental aspects, is employed to calculate their compositive scores, in which the integrated weights of indicators combine the experts’ knowledge and opinions with the entropy information of numerical performances of alternative trigeneration configurations. The sensitivities of exergy-cost and exergy-carbon performances on operation time, carbon footprint of biomass, and interest rate are implemented to discuss their influences. The consideration of energy level increases the cost and carbon footprint of power from fuel cells by around 30%. The carbon cost of system power, chilled water, and hot water account for 5.18%, 6.89%, and 5.25% of their corresponding total exergy-carbon costs. The integration of biomass gasification, fuel cell, gas turbine, and heat exchanger achieves the best comprehensive performance.

A multisource data‐driven approach for carbon footprint analysis of remanufacturing systems

AbstractThe remanufacturing industry is experiencing significant growth, resulting in a substantial increase in its carbon footprint. Unfortunately, this issue is not receiving adequate attention at present. Moreover, existing methods for analyzing carbon footprints in remanufacturing systems are inefficient and inaccurate due to the complex relationship between carbon emissions and the dynamic nature of the systems. In this paper, we propose a multisource data‐driven approach for carbon footprint analysis of remanufacturing systems. First, we examine the relationship between carbon emissions and the uncertainty surrounding end‐of‐life (EOL) product components, as well as the corresponding resource recovery strategies, such as reuse, recycle, and remanufacture. This analysis allows us to define the carbon footprint boundary of remanufacturing systems. Next, we establish a carbon footprint model for recycling strategies using recycling data. Additionally, we propose a carbon footprint model for remanufacture strategies by utilizing the back propagation neural network based on particle swarm optimization algorithm and data from the materials flow, energy flow, and waste flow of these strategies. By combining these models, we can accurately calculate the carbon footprint of remanufacturing systems. To demonstrate the effectiveness of our approach, we present a case study on the remanufacturing of an EOL automobile engine. This case study showcases how our methodology can be utilized as a valuable tool for analyzing carbon footprints and identifying strategies to reduce carbon emissions in remanufacturing systems.

Spatiotemporal variations of water, land, and carbon footprints of pig production in China

Pig production not only consumes large water and land, but also emits substantial greenhouse gases. Scholars have used environmental footprint, including water, land, and carbon footprints, to assess the environmental impacts of pig production. However, there is a lack of simultaneous analysis of water, land, and carbon footprints for pig production, particularly in China, the leading pig breeding and consumption country in the world. In this study, we used environmental footprint analysis to develop a water-land-carbon footprint model for pig production system from the life cycle perspective. We also examined virtual water, land and carbon flows embedded in trade. The life cycle of pig production mainly involves feed crop cultivation, feed production, and pig breeding. Then we analyzed the water, land, and carbon footprints of the pig production in China from 1990 to 2018. The findings indicated that both the total water footprint and the total land footprint increased, whereas the total carbon footprints fluctuated over the years. All the unit footprints generally decreased. Feed crop cultivation had a significant impact on the footprints. Regarding the spatial characteristics in China, all the three total footprints were generally higher in the southeast compared to the northwest. While the unit footprints in different provinces exhibited spatial heterogeneity, primarily attributed to the different feed consumptions. Overall, the trade of feed crop shifted virtual water, land and carbon flows from the north to the south, reducing the three total footprints on the whole. To achieve a resource-saving and eco-friendly pig production in China, we proposed some potential recommendations, including improving feed crop cultivation techniques, reducing feed conversion ratio, implementing precision feeding, and managing manure. We hope not only mitigate the environmental impacts of China’s pig production, but also provide references for achieving agricultural sustainability in other regions of the world.

Evaluation of water resources sustainable development in karst area based on the improved emergy ecological footprint model: A case study of Yunnan Province

Abstract This study combined emergy theory with water ecological footprint to analyze and evaluate the sustainable development status of water resources in Yunnan Province from 2012 to 2021. The water pollutant account and the water product account were supplemented to the water ecological footprint. The results showed that the water resources in Yunnan Province were generally in an ecological deficit during the study period, but their absolute values were very small. The ecological pressure index of water resources was high, while the sustainable utilization index of water resources was low, indicating that the water resources were in a state of unsustainable utilization. However, the emergy ecological footprint of water resources and the emergy ecological footprint of water resources of 10,000 Yuan GDP showed a decreasing trend, and the eco-economic coordination index of water resources was relatively high, indicating that the water resources will show a sustainable development trend in the future. The sustainable development of water resources was influenced by both natural ecological factors and social factors. Therefore, suggestions such as improving the utilization rate of water resources development, developing a green low-carbon economy, preventing and controlling water pollution, and protecting the water environment are conducive to promoting the sustainable development of water resources.

Ecological risk evaluation of ammonia nitrogen pollution in China based on the ecological grey water footprint model

The biosafety criteria of ammonia nitrogen (NH3–N) exhibit uncertainties, posing challenges to the assessment of the hazard of social NH3–N load to aquatic ecosystem. To evaluate this ecological hazard in China, an ecological grey water footprint (E-GWF) model is designed based on the uncertainty analysis theory. In the E-GWF model, the acute toxicity is quantified via short-term E-GWF (E-GWFs) and acute risk (AR), while its chronic toxicity is quantified via long-term E-GWF (E-GWFl) and chronic risk (CR). Results show the following. (i) Compared with the conventional GWF, the E-GWF performs better in the uncertainty analysis of the biosafety threshold, and it is more effective in ecological risk evaluation and environment planning. (ii) The E-GWFs and E-GWFl of NH3–N in China are 309.4 and 2382.5 billion m3, respectively. Regions with large E-GWF are concentrated in the east and south, while regions with small E-GWF are concentrated in the north and west. (iii) The ecological risks of NH3–N in Shanghai City, Tianjin City, Ningxia Province, Hebei Province, Jiangsu Province, Shanxi Province, and Shandong Province belong to the “High” grade. The ecological risks of NH3–N in Tibet and Qinghai Province belong to the “Negligible” grade. (iv) The ecological risk of NH3–N in China is mostly determined by industrial and domestic pollution. (v) To control the risk within allowable grades, the social NH3–N pollution load of China should be decreased to 988.7 kilotons.

Ecological effect life cycle assessment of house buildings based on emergy footprint model

Construction is an important sector for climate action. The construction, operation and maintenance, demolition and disposal stages of house buildings consume many resources and have a significant impact on society, the economy and the environment. To assess such efforts, we propose the emergy footprint model of house buildings, which can quantitatively analyse the ecological effect in the house buildings life cycle. The research shows the following. China’s ecological efficiency of the housing sector is characterized by improvement. In the house building fifty-year life cycle, the emergy footprint of the operation and maintenance stage is the largest (75.92%), followed by the construction stage (21.95%), but the emergy footprint intensity of the latter is 4.82 times that of the former. Reducing energy consumption and carbon dioxide emissions in the operation and maintenance stage is the key to reducing the life cycle emergy footprint of house buildings. The ecological impact coefficient of house buildings is negatively exponentially correlated with their service life. It reaches ecological break-even when the service period of the house building is equal to 36.73 years. If the house building is demolished after less than nine years of service, the impact is extremely unfavourable.

Downscaling the APSIM crop model for simulation at the within-field scale

CONTEXTMost crop models are designed for point-based modeling and to simulate agronomic variables on their native spatial footprint, i.e. typically as a uniform field-scale value. Precision agriculture needs crop model simulations at sub-field scales to support differential management application. Spatialization processes are used to change the simulation scale of crop models. OBJECTIVEThe objective of this study is to investigate the spatialization of a complex crop model by using a spatial calibration approach to modify its native spatial footprint and to evaluate if it is relevant to use this kind of crop model at the within-field scale. METHODSAPSIM was spatialized to simulate durum wheat yield at different spatial scales (field, within-field and site-scale) on an experimental field under Mediterranean conditions in southern Italy. Ancillary soil data were used to derive potential management (modeling) zones at different scales, which were then used to spatially calibrate soil and biomass parameters in APSIM to spatially predict yield in two different production years (one year was used for calibration and the other for evaluation). Spatialized crop model performances were evaluated using the spatial balanced accuracy (SBA) score, a metric to evaluate the global preservation of patterns between maps. RESULTS AND CONCLUSIONSThe spatial structure of the yield data influenced the effectiveness of the spatial calibration process. When the agronomic variable (durum wheat yield) was spatially structured, a spatialized APSIM approached performed best (5-zone modeling scale, SBA = 0.17) and outperformed the field-scale (native footprint) model (SBA = 0.19). In contrast, when the target agronomic variable was more random (less spatially structured), the uniform field-scale modeling performed best and spatial calibration had no benefit. The spatialized APSIM performances were mainly based on the reliability of the delineated zones that undeniably affected the quality of the spatialized model outputs. Thus, more research is needed on how best to model scale-dependent processes to have more reliable modeling at the within-field scale. SIGNIFICANCEBased on the example of a complex crop model like APSIM, this study showed that spatial calibration can be effective and has a role to play in the spatialization of complex crop models.

Seasonal Dynamics of Flux Footprint for a Measuring Tower in Southern Taiga via Modeling and Experimental Data Analysis

This paper reports on the location of sources contributing to a point flux measurement in the southern taiga, Russia. The measurement tower is surrounded by a coniferous forest with a mean aerodynamically active height of 27 m (h). Aerodynamical parameters of the forest, such as displacement height d and aerodynamic roughness z0, derived from wind speed profile measurements for 2017–2019, were used to estimate the seasonal and daily behavior of the flux footprint. Two analytical footprint models by Schuepp et al. and by Kormann and Meixner driven by d and z0 were used to estimate the footprint for canopy sources. The Lagrangian simulation (LS) approach driven by flow statistics from measurements and modeling was used to estimate the footprint for ground-located sources. The Flux Footprint Prediction (FFP) tool for assessing canopy flux footprint (Kljun et al.) applied as the option in the EddyPro v.7 software was inspected against analytical and LS methods. For model comparisons, two parameters from estimated footprint functions were used: the upwind distance (fetch) of the peak contribution in the measured flux (Xmax) and the fetch that contributed to 80% of the total flux (CF80). The study shows that Xmax varies slightly with season but relies on wind direction and time of day. All methods yield different Xmax values but fall in the same range (60–130 m, around 2–5 h); thus, they can estimate the maximum influence distance with similar confidence. The CF80 values provided by the FFP tool are significantly lower than the CF80 values from other methods. For instance, the FFP tool estimates a CF80 of about 200 m (7 h), whereas other methods estimate a range of 600–1100 m (25–40 h). The study emphasizes that estimating the ground source footprint requires either the LS method or more complex approaches based on Computational Fluid Dynamics (CFD) techniques. These findings have essential implications in interpreting eddy-flux measurements over the quasi-homogeneous forest.

Does globalization mitigate environmental degradation in selected emerging economies? assessment of the role of financial development, economic growth, renewable energy consumption and urbanization.

While the acceleration of globalization in newly developing (emerging) economies contributes positively to economic developments on the one hand, it is a research topic that can have an impact on environmental pollution on the other hand. Therefore, this study analyzes the impact of globalization on environmental pollution for 14 emerging economies in the 1991-2018 period by including economic growth, financial development, renewable energy consumption, and urbanization in the ecological footprint model. In addition to the AMG forecaster, Driscoll-Kraay, PCSE, and FGLS estimation techniques are used for long-term forecasting. Causal linkages among variables are analyzed by the Dumitrescu-Hurlin panel bootstrap causality test. The findings show that the series are cointegrated, that is, a long-term relationship between the variables. In the long term, globalization and renewable energy consumption reduce environmental pollution, while economic growth and financial development play a role in encouraging environmental pollution. Causality analysis enumerates a causality from economic growth and financial development to environmental pollution, as well as a two-way causality between globalization and environmental pollution and renewable energy consumption and environmental pollution. Empirical findings can offer important implications for policies that will reduce environmental pollution in these countries.

Ecological footprint model of heavy metal pollution in water environment based on the potential ecological risk index

Social heavy metal pollution poses a significant threat to aquatic ecosystems. Heavy metals are easily adsorbed by sediments and have cumulative effects on aquatic organisms, which is different with the hypothesis of the conventional ecological footprint model that the pollutants are independently degraded by water bodies. To solve this problem, an ecological footprint for heavy metal pollution (EFHM) is constructed based on the potential ecological risk index (PERI). EFHM is defined as the sediment area to control the cumulative ecological risk of heavy metals within the allowable limit. And then, EFHM uses ecological footprint index (EFI) and ecological footprint contribution rate (EFCR) to quantify the hazard of social heavy metal load and recognize the key risk factor. EFHM is applied for assessing the heavy metal pollution of Central China. The results show that (i) the EFHM model can effectively evaluate the cumulative ecological hazards of heavy metals in sediment. (ii) The EFHM values of Central China in 2015 and 2020 are 20,764.56 and 17,358.59 km2, respectively. (iii) Compared with 2015, the EFI values of Hunan Province and Jiangxi Province in 2020 decrease from 1.53 to 0.87 to 1.23 and 0.39, respectively, both of which are improved by one grade. The EFI values of Hubei Province increases from 0.42 to 1.34, which is deteriorated by one grade. (iv) In 2020, both of the key risk factors of Hunan Province and Hubei Province are Hg, and the crucial hazard source of Jiangxi Province is Cd. (v) The mine pollution control in Central China should be further consolidated, and the wastewater treatment of electronics and machinery industries should be strengthened.

Ammonia emissions of an open-lot dairy in North China Plain: Field experiments and source inversion

Significant uncertainties exist in China's ammonia emission inventories, particularly in livestock husbandry. Three phases of field experiments were conducted in summer and winter in an open-lot dairy to estimate ammonia emissions. The dairy is located in the northwest suburb of Beijing (40°9′47.88″N, 116°17′1.63″E, 47 m a.s.l.) with about 400 cows, a representative condition in North China Plain. The exploratory phase lasted from May 29 to July 18, 2016; the intensive phases lasted from December 16, 2016, to January 10, 2017, and from June 1 to July 2, 2018. Ammonia concentrations were measured upwind and downwind of the dairy, along with wind statistics. After quality control, the data were used to determine the contribution of the prescribed source areas to the measured ammonia concentrations using an analytical footprint model. Therefore, the source intensity was inversely derived under the assumption that the emission rate was spatially homogeneous over the source areas. Diurnal variation of ammonia emission from the dairy was revealed, with peaks around noon being 0.66 g s −1 in winter and 0.80 g s −1 in summer. The total emissions of the dairy are 0.38 ± 0.13 g s −1 in winter and 0.46 ± 0.10 g s −1 in summer, corresponding to the emission factors 82 ± 27 g head−1d −1 in winter and 99 ± 23 g head−1d −1 in summer. The results were comparable to previous studies using similar methods and to those in China's ammonia source inventory.

Development of a tire testing machine for evaluating the performance of tractor tires based on the soil compaction

Soil compaction, which is more effective than soil shear owing to the weight of tractor tires, reduces sugarcane cultivation. Instead of a field study, a single-wheel tester is more suitable for studying the tractor tire tracks on a soil bin in a laboratory. However, the soil compaction measurement is time consuming and analyzes the bulk density only at the sampling point of the track. In this study, a tire-testing machine (TTM) was developed to evaluate tractor tire performance based on soil compaction. A 3D scanner was employed to model the tractor tire track on a sandy clay loam soil, which is suitable for sugarcane planting in Thailand. Mathematical models for soil bulk density prediction were proposed and used to transform the 3D footprint model into a 3D color contour of the soil bulk density. The soil compaction effect of tractor tires at three different tread depths was evaluated. It was found that deeper treads caused higher soil bulk density than shallow treads. Static tires induce greater soil bulk density than rolling tires. This novel TTM with measurement techniques is a novel method for evaluating the tire-soil compaction in crop fields. It can also guide farmers to purchase suitable agricultural tires.

Quantifying plastic use and waste footprints in SIDS: Application to Seychelles

Small Island Developing States (SIDS) are the most vulnerable territories to marine litter and plastic waste pollution. The magnitude of the issue and ways of action can only be known if the material flows and their origins are correctly estimated. Unfortunately, small territories are often left outside the global ecological footprint databases and models. The present research aims at quantifying the plastic footprint of Seychelles through a standard Environmentally-Extended Input-Output Analysis (EEIOA) and multi-regional input-output (MRIO) approach combining international and domestic data in the south-west Indian Ocean region. The results of several model specifications and industrial classifications are compared to the literature findings and show that SIDS may display the same level of plastic use and waste per capita as high-income countries, without the same infrastructure of waste treatment. A few services and exporting sectors concentrate the bulk of the territorial quantity of plastics used throughout the economy. The multi-regional Indian Ocean context helps to foresee potential joint actions in order to reduce the plastic footprint of SIDS.

Architectural CO2 Footprint Tool: Designing Sustainable Computer Systems With an Architectural Carbon Modeling Tool

Given the performance and efficiency optimizations realized by the computer systems and architecture community over the last decades, the dominating source of computing’s carbon footprint is shifting from operational emissions to embodied emissions. These embodied emissions owe to hardware manufacturing and infrastructure-related activities. Despite the rising embodied emissions, there is a distinct lack of architectural modeling tools to quantify and optimize the end-to-end carbon footprint of computing. This work proposes ACT, an architectural carbon footprint modeling framework, to enable carbon characterization and sustainability-driven early design space exploration. Using ACT we demonstrate optimizing hardware for carbon yields distinct solutions compared to optimizing for performance and efficiency. We construct use cases, based on the three tenets of sustainable design—Reduce, Reuse, Recycle—to highlight future methods that enable strong performance and efficiency scaling in an environmentally sustainable manner

Significant race and gender differences in anterior cruciate ligament tibial footprint location: a 3D-based analysis

BackgroundThe aim of the present study was to identify potential race- or gender-specific differences in anterior cruciate ligament (ACL) tibial footprint location from the tibia anatomical coordinate system (tACS) origin, investigate the distances from the tibial footprint to the anterior root of the lateral meniscus (ARLM) and the medial tibial spine (MTS), determine how reliable the ARLM and MTS can be in locating the ACL tibial footprint, and assess the risk of iatrogenic ARLM injuries caused by using reamers with various diameters (7–10 mm).Patients and methodsMagnetic resonance images of 91 Chinese and 91 Caucasian subjects were used for the reconstruction of three-dimensional (3D) tibial and ACL tibial footprint models. The anatomical coordinate system was applied to reflect the anatomical locations of scanned samples.ResultsThe average anteroposterior (A/P) tibial footprint location was 17.1 ± 2.3 mm and 20.0 ± 3.4 mm in Chinese and Caucasians, respectively (P < .001). The average mediolateral (M/L) tibial footprint location was 34.2 ± 2.4 mm and 37.4 ± 3.6 mm in Chinese and Caucasians, respectively (P < .001). The average difference between men and women was 2 mm in Chinese and 3.1 mm in Caucasians. The safe zone for tibial tunnel reaming to avoid ARLM injury was 2.2 mm and 1.9 mm away from the central tibial footprint in the Chinese and Caucasians, respectively. The probability of damaging the ARLM by using reamers with various diameters ranged from 0% for Chinese males with a 7 mm reamer to 30% in Caucasian females with a 10 mm reamer.ConclusionsThe significant race- and gender-specific differences in the ACL tibial footprint should be taken in consideration during anatomic ACL reconstruction. The ARLM and MTS are reliable intraoperative landmarks for identifying the tibial ACL footprint. Caucasians and females might be more prone to iatrogenic ARLM injury.Level of evidence: III, cohort study.Trial registration: This study has been approved by the ethical research committee of the General Hospital of Southern Theater Command of PLA under the code: [2019] No.10.

Towards the development of an improved mass balance and water quality index based grey water footprint model

With the increasing pressure on water resources, there is a need to better understand water use throughout the supply chain of goods and services to identify opportunities to alleviate this stress. Therefore, a proper assessment of the water footprint is necessary. The grey water footprint (GWF), defined as the freshwater required to dilute excess pollutants discharged into the environment, is an important indicator and has various models, which are disputed on their ability to effectively capture the extent of water pollution. This study suggests an improved model based on two stages. The first stage consists of a mass balance used to amend the concentrations of the pollutants. As for the second stage, it consists of calculating the GWF based on the Water Quality Index (WQI) which is the most popular water assessment tool used by researchers. The suggested model allows a better breakdown of the GWF required to assimilate the pollution to achieve different levels of water quality as it outputs 5 different ranges based on the water quality sought as opposed to a single value or range traditionally obtained using existing models. In addition, the comparison between the improved model and the existing ones demonstrates that the existing models underestimate the GWF if a medium to excellent water quality is to be sought.

Can upscaling ground nadir SIF to eddy covariance footprint improve the relationship between SIF and GPP in croplands?

Ground solar-induced chlorophyll fluorescence (SIF) is important for the mechanistic understanding of the dynamics of vegetation gross primary production (GPP) at fine spatiotemporal scales. However, eddy covariance (EC) observations generally cover larger footprint areas than ground SIF observations (a bare fiber with nadir), and this footprint mismatch between nadir SIF and GPP could complicate the canopy SIF-GPP relationships. Here, we upscaled nadir SIF observations to EC footprint and investigated the change in SIF-GPP relationships after the upscaling in cropland. We included 13 site-years data in our study, with seven site-years corn, four site-years soybeans, and two site-years miscanthus, all located in the US Corn Belt. All sites’ crop nadir SIF observations collected from the automated FluoSpec2 system (a hemispheric-nadir system) were upscaled to the GPP footprint-based SIF using vegetation indices (VIs) calculated from high spatiotemporal satellite reflectance data. We found that SIF-GPP relationships were not substantially changed after upscaling nadir SIF to GPP footprint at our crop sites planted with corn, soybean, and miscanthus, with R2 change after the upscaling ranging from -0.007 to 0.051 and root mean square error (RMSE) difference from -0.658 to 0.095 umol m − 2s − 1 relative to original nadir SIF-GPP relationships across all the site-years. The variation of the SIF-GPP relationship within each species across different site-years was similar between the original nadir SIF and upscaled SIF. Different VIs, EC footprint models, and satellite data led to marginal differences in the SIF-GPP relationships when upscaling nadir SIF to EC footprint. Our study provided a methodological framework to correct this spatial mismatch between ground nadir SIF and GPP observations for croplands and potentially for other ecosystems. Our results also demonstrated that the spatial mismatch between ground nadir SIF and GPP might not significantly affect the SIF-GPP relationship in cropland that are largely homogeneous.

Modelling carbon footprint, emission, and sequestration of kenaf cultivation and fiber processing and utilization into automotive components

This study aimed to model carbon footprint, carbon dioxide sequestration, and emissions from kenaf cultivation as well as utilization of core and bast as biomaterials in the automotive industries and their environmental implications. It also considers the selection of suitable processing methods to improve the environmental performance of automotive parts. The data were obtained from three areas, and each area was divided into three cultivated sub areas for the carbon footprint model. Data regarding the conversion of kenaf to fibre when using kenaf high decorticator machines were provided from Kenaf Processing and Marketing Centre (CMPC) in Malaysia. The results showed that the total estimated quantity of carbon footprint of kenaf cultivation in the studied areas as well as the farms was about 0.750 tonne CO2/tonne. In addition, the total estimated quantity of carbon sequestration of automotive components in the studied states as well as the district was approximately 180,000 tonne CO2/tonne/hectare/year in addition to carbon sequestered by soil in hectare of cultivated kenaf. The practices of kenaf cultivation, processing, and utilization into automotive components is essential for the environment and will assist in the mitigation of climate change risks.

What are the main environmental impacts and products contributing to the biodiversity footprint of EU consumption? A comparison of life cycle impact assessment methods and models

PurposeRecent developments in life cycle impact assessment (LCIA) target at better addressing biodiversity impacts, including the extended modeling of drivers of biodiversity loss. This led to the development of multiple LCIA methods addressing the area of protection of ecosystem quality (i.e, biodiversity loss) over time. This paper aims at systematically comparing available operational LCIA methods and models for assessing the main drivers of biodiversity impacts of EU consumption and unveiling similarities and differences among current methods.MethodsThis paper compares the biodiversity impacts of EU consumption by implementing eight LCIA methods and models: five full LCIA methods (namely, LC-IMPACT, Impact World + , Ecological Scarcity 2013, ReCiPe 2016, and Stepwise), a land-use intensity-specific LCIA model, and two approaches based on the GLOBIO model. The EU Consumption Footprint model is adopted as case study. The comparative analysis between the assessed methods aimed at identifying convergent and divergent results regarding the drivers of biodiversity impacts of EU consumption. The analysis focused on four different levels: impact category, representative product (modeled consumed products), inventory process, and elementary flow. The agreement among the methods in defining an element as relevant was evaluated. Finally, gaps among methods were assessed in terms of coverage of impact categories and elementary flows.Results and discussionThe analysis unveiled that there is a certain level of agreement among available LCIA methods and models regarding the most contributing impact categories and products to the overall biodiversity footprint due to EU consumption. Land use, climate change, and ecotoxicity had a major contribution to overall impacts, thereby highlighting their role as drivers of biodiversity loss. Biodiversity impacts were due to a limited number of consumed products, where food (meat), mobility, and household goods were identified as top contributors. Most contributing inventory processes and elementary flows were associated to most contributing representative products (e.g, animal feed). The relevance and presence of elementary flows in LCIA methods and models were heterogeneous for most of the impact categories.ConclusionsThe results of this study highlight the importance of impact category coverage in the assessment of biodiversity impacts. Limited coverage of impact categories (e.g, methods limited to assess land use) might underestimate the impacts of other drivers of biodiversity loss, especially climate change and ecotoxicity. Further efforts are required to assess the effects of spatial regionalization and the inclusion of missing drivers, recently developed in LCIA.

Assessing Environmental Sustainability Based on the Three-Dimensional Emergy Ecological Footprint (3D EEF) Model: A Case Study of Gansu Province, China

Quantifying the supply and demand relationship between human social consumption and natural ecosystem resources is an effective way to assess the sustainability of ecosystem services. This paper introduces the concepts of footprint size (EFsize) and depth (EFdepth) to emergy analysis to establish a three-dimensional emergy ecological footprint (3D EEF) model and evaluates the environmental sustainability development of Gansu Province from 2001 to 2020. The results show that the minimum value of the 3D emergy ecological footprint (EEF3D) of Gansu Province was 5.98 × 107 hm2 and that the maximum value was 1.41 × 108 hm2. The EEFdepth was slightly more than one in 2015 and 2016, and the ecological resources were in deficit. However, the ecological service system from 2001 to 2020 was almost in a sustainable state. The region’s emergy carrying capacity (ECC) and emergy ecological footprint (EEF) from 2021 to 2030 are predicted using the ARIMA and GM (1, 1) models. The projections show that the ECC and EEF will increase year by year from 2021 to 2030. In the short term, the flow of natural capital can satisfy the development requirements. Finally, policy recommendations are provided for the ecological sustainability of the region.