Search

Recent policy initiatives have placed a strong focus on the use of agricultural soils for atmospheric CO2 removal by adopting practices for sequestering and storing SOC. In the UK, changes in agricultural land use, such as the integration of woody species in the form of hedgerows--lines of regularly trimmed shrubs commonly used to delimit agricultural fields--, have been recommended for climate change mitigation. The Climate Change Committee has proposed a 40% increase in hedgerow length across the country as a key contribution to net-zero targets. In England, this would equate to 193,000 km of newly planted hedgerows. However, the contribution of hedgerow planting to reaching net-zero goals remains unclear due to a lack of data on the rate at which CO2 is taken up and stored in the soil beneath them. In our study, seventy-eight hedgerows across six different pedo-climatic conditions in England were classified into four age categories. Soil organic carbon (SOC) stocks were quantified at 10 cm intervals for the top 50 cm of soil beneath hedgerows and in adjacent grassland fields. Moreover, we examined the distribution of SOC among particle-size fractions to investigate how hedgerow planting may influence SOC dynamics by affecting the quality and long-term stability of organic matter in soils, particularly to illustrate why hedgerow-associated SOC stocks are rapidly lost after hedgerow removal. SOC stocks beneath hedgerows were higher than adjacent fields for all age categories and hedgerows stored an average additional 40% SOC stock in the top 50 cm of soil compared to adjacent fields and 30% in the top 30 cm of soil. The additional SOC stock beneath hedgerows was 40.9 Mg C ha-1 at 0-50 cm depth, or 6.1 Mg C km-1. We used a 37-year-old SOC sequestration rate to show that if England were to reach its goal of 40% increase in hedgerow length, 6.3 Tg of CO2 will be sequestered and stored in the soil over 40 years (9.9 Tg with aboveground biomass). However, it will take ~200 years to reach this target with current rates of planting in national public agri-environment schemes. These results contribute measurable outcomes towards the estimate of targets for net-zero 2050 and the extent of ecosystem services provision by hedgerow planting in agricultural landscapes.

This paper explores farmers’ and land managers’ perceptions of the emerging agricultural soil carbon market in the UK and examines their willingness to adopt soil health management practices to enhance and/or maintain soil carbon stocks and enthusiasm for and interest in participation in soil carbon sequestration schemes. Data were collected through online questionnaires administered to 100 farmers and six organisations responsible for the operationalisation and development of carbon codes in the UK using online questionnaires. The results indicate that farmers’ prior adoption of practices that promote soil health does not necessarily translate into a willingness to adopt additional practices and/or “buy into” soil carbon sequestration schemes. Farmers have reservations about planning and implementing soil carbon projects due to the terms and conditions associated with participation in the emerging UK agricultural soil carbon market. Although the carbon market may attract new entrants, early adopters of soil health management practices are likely to be excluded from soil carbon sequestration schemes established by public and private sector actors based on additionality criteria. The results of this study also suggest early adopters’ expectations regarding their scope to derive benefits from participation in the carbon market are at odds with the demands of the carbon market as articulated by the carbon codes driving the development and growth of the market. These results highlight that the key role that early adopters may play in encouraging new entrants to engage with the carbon market should not be underestimated. It contends that enhancing the transparency, robustness, and integrity of the carbon market hinges on incentivising early adopters to adopt additional practices that promote soil health and facilitate their participation in the market, alongside new entrants. The paper argues that kick-starting and supporting the growth of the agricultural soil carbon market is contingent on reconciling farmers’ expectations with the demands of the market, during an initial transition period, through flexible implementation of rules and regulations outlined by carbon codes regarding soil carbon sequestration and storage in agricultural soils.

Agriculture plays a central role in achieving most Sustainable Development Goals (SDGs). Sustainable intensification (SI) of agriculture has been proposed as a promising concept for safeguarding global food security, while simultaneously protecting the environment and promoting good quality of life. However, SI often leads to context-specific sustainability trade-offs. Operationalising SI thus needs to be supported by transparent sustainability assessments. In this article, we propose a general systematic approach to developing context-specific frameworks for integrated sustainability assessment of agricultural intensity change. Firstly, we specify a comprehensive system representation for analysing how changes in agricultural intensity lead to a multitude of sustainability outcomes affecting different societal groups across geographical scales. We then introduce a procedure for identifying the attributes that are relevant for assessment within particular contexts, and respective indicator metrics. Finally, we illustrate the proposed approach by developing an assessment framework for evaluating a wide range of intensification pathways in Europe. The application of the approach revealed processes and effects that are relevant for the European context but are rarely considered in SI assessments. These include farmers’ health, workers’ living conditions, cultural heritage and sense of place of rural communities, animal welfare, impacts on sectors not directly related to agriculture (e.g., tourism), shrinking and ageing of rural population and consumers’ health. The proposed approach addresses important gaps in SI assessments, and thus represents an important step forward in defining transparent procedures for sustainability assessments that can stimulate an informed debate about the operationalisation of SI and its contribution towards achieving SDGs., Environmental Science & Policy, 137, ISSN:1462-9011, ISSN:1873-6416

This deliverable provides a General Framework for the BESTMAP Policy Impact Assessment Modelling (BESTMAP-PIAM) toolset.The BESTMAP-PIAM is based on the notion of defining (a) a typology of agricultural systems, with one (or more) representative case study (CS) in each major system; (b) mapping all individual farms within the case study to a Farm System Archetype (FSA) typology; (c) model the adoption of agri-environmental schemes (AES) within the spatially-mapped FSA population using Agent Based Models (ABM), based on literature and a survey with sufficient representative sample in each FSA of each CS, to elucidate the non-monetary drivers underpinning AES adoption and the relative importance of financial and non-financial/social/identity drivers; (d) linking AES adoption to a set of biophysical, ecological and socio-economic impact models; (e) upscaling the CS level results to EU scale; (f) linking the outputs of these models to indicators developed for the post-2020 CAP output, result and impact reports; (g) visualizing outputs and providing a dashboard for policy makers to explore a range of policy scenarios, focusing on cost-effectiveness of different AES.

This document has five main sections: the first one, “Developing the needs assessment protocol” which explains how we approached to different stakeholders in order to define and analyse their needs and capacities; the second section contains the report of of the interviews conducted by RISE and present the needs of Policy Makers; section three explains the needs of expert practitioners identified during the online workshop (14th and 15th of July 2020); section four presents the needs of biophysical modeling community and section five explains the needs of ABM modellers identified from recent scholarly workshops. The results of this analysis will be taken under consideration and co-design and co-development processes.

1. Gold mining has rapidly increased across the Amazon Basin in recent years, especially in the Guiana shield, where it is responsible for >90% of total deforestation. However, the ability of forests to recover from gold mining activities remains largely unquantified. 2. Forest inventory plots were installed on recently abandoned mines in two major mining regions in Guyana, and re‐censused 18 months later, to provide the first ground‐based quantification of gold mining impacts on Amazon forest biomass recovery. 3. We found that woody biomass recovery rates on abandoned mining pits and tailing ponds are among the lowest ever recorded for tropical forests, with close to no woody biomass recovery after 3–4 years. 4. On the overburden sites (i.e. areas not mined but where excavated soil is deposited), however, above‐ground biomass recovery rates (0.4–5.4 Mg ha−1 year−1) were within the range of those recorded in other secondary forests across the Neotropics following abandonment of pastures and agricultural lands. 5. Our results suggest that forest recovery is more strongly limited by severe mining‐induced depletion of soil nutrients, especially nitrogen, than by mercury contamination, due to slowing of growth in nutrient‐stripped soils. 6. We estimate that the slow recovery rates in mining pits and ponds currently reduce carbon sequestration across Amazonian secondary forests by ~21,000 t C/year, compared to the carbon that would have accumulated following more traditional land uses such as agriculture or pasture. 7. Synthesis and applications. To achieve large‐scale restoration targets, Guyana and other Amazonian countries will be challenged to remediate previously mined lands. The recovery process is highly dependent on nitrogen availability rather than mercury contamination, affecting woody biomass regrowth. The significant recovery in overburden zones indicates that one potential active remediation strategy to promote biomass recovery may be to backfill mining pits and ponds with excavated soil.

Half of the European Union (EU) land and the livelihood of 10 million farmers is threatened by unsustainable land-use intensification, land abandonment and climate change. Policy instruments, including the EU Common Agricultural Policy (CAP) have so far failed to stop this environmental degradation. BESTMAP will: 1) Develop a behavioural theoretical modelling framework to take into account complexity of farmers’ decision-making; 2) Develop, adapt and customize a suite of opensource, flexible, interoperable and customisable computer models linked to existing data e.g. LPIS/IACS and remote sensing e.g. Sentinel-2; 3) Link economic, individual-farm agent-based, biophysical ecosystem services and biodiversity and geostatistical socio-economic models; 4) Produce a simple-to-use dashboard to compare scenarios of Agri-Environmental Schemes adoption; 5) Improve the effectiveness of future EU rural policies’ design, monitoring and implementation.

Brazil contains two-thirds of remaining Amazonian rainforests and is responsible for the most Amazon forest loss. Primary forest loss in the Brazilian Amazon has declined considerably since 2004 but secondary forest loss has never been quantified. We use a recently developed high-resolution land use/land cover dataset to track secondary forests in the Brazilian Amazon over 14 yr, providing the first estimates of secondary forest loss for the region. We find that secondary forest loss increased by (187 ± 48)% from 2008 to 2014. Moreover, the proportion of total forest loss accounted for by secondary forests rose from (37 ± 3)% in 2000 to (72 ± 5)% in 2014. The recent acceleration in secondary forests loss occurred across the entire region and was not driven simply by increasing secondary forest area but probably a conscious preferential shift towards clearance of a little-protected forest ecosystem (secondary forests). Our results suggest that secondary forests loss has eased deforestation pressure on primary forests. However, this has been at the expense of a lost carbon sequestration opportunity of 2.59–2.66 Pg C over our study period. Deforestation in the Brazilian Amazon affects both older (primary) and younger (secondary) forests. This study finds that most forest loss over the period 2008–2014 was from secondary forests and that the almost 190% rise in deforestation buffered losses from primary forests.

This document is the first version of the Guidelines and protocols harmonizing activities across case studies of the H2020 BESTMAP project. It is intended to be updated in month 40 (D1.8).

Model-based analyses can effectively contribute to investigate leverage points for sustainability transformations in agriculture. They allow for a systematic assessment of policies under changing environmental, economic, or institutional conditions and can be used to evaluate the efficiency of different policy designs. This makes them an important tool for critically evaluating agricultural policies and shaping them appropriately to achieve the desired effect. For analyzing agricultural systems, agent-based modeling is particularly useful as it allows to represent individual farmers the crucial actors at the landscape level. This approach can explicitly incorporate farmer behavior to map, for example, the conditions for adopting sustainable practices that lead to more diverse agroecosystems. In order to provide policy makers with an effective tool, an adequate representation of farmers' decision-making is crucial. However, formalizing empirically observed farmer behavior in model rules is difficult because, as in all models describing human behavior, complex decision-making must be simplified into clear cause-effect relationships. Following established behavioral theories, on the other hand, also entails difficulties: First, theories often consider only certain aspects of decision-making and fall short when it comes to incorporating the multiple influences that farmers face. In addition, a rather low degree of formalization of theories as well as limited data availability for model parameterization are problematic. As a consequence, many ABMs remain rather stylized, reducing the external validity of the emerging results and leading to limited potential to help policy makers identify suitable measures for a sustainable transformation of agriculture. We aim to address this gap with a methodological contribution by developing an empirically driven decision framework for the adoption of agri-environmental schemes (AES). To capture the most important processes of the decision-making in this context, we first collected possible influence factors in an extensive literature search. Based on this overview, we derived a semi-structured interview protocol covering open questions and a standardized questionnaire that provided the basis for an interview campaign in five case study regions across Europe (Czech Republic, Germany, Serbia, Spain and United Kingdom). With the help of local case study experts, we were able to derive key factors for farmer decision-making on AES adoption from the obtained qualitative and quantitative data. We condensed these observations in a formalized conceptual framework that covers a three-step decision process. It incorporates (1) whether farmers are in general open toward the adoption of specific AES, (2) which of their fields are suitable for AES adoption and (3) the final deliberation whether farmers adopt specific AES that is not only driven by economic factors but also includes ecological and social aspects as well as whether an AES fits to the established farm practice. To parameterize the framework and capture regional differences more specifically, we plan to conduct a discrete choice experiment with which the influence factors can be further quantified. We conclude with illustrating examples of research questions that can be addressed using this framework.

There is growing interest around the world in more effectively linking public payments to the provision of public goods from agriculture. However, published evidence syntheses suggest mixed, weak or uncertain evidence for many agri-environment scheme options. To inform any future “public money for public goods” based policy, further synthesis work is needed to assess the evidence-base for the full range of interventions currently funded under agri-environment schemes. Further empirical research and trials should then focus on interventions for which there is mixed or limited evidence. Furthermore, to ensure the data collected is comparable and can be synthesised effectively, it is necessary to reach agreement on essential variables and methods that can be prioritised by those conducting research and monitoring. Future policy could then prioritise public money for the public goods that can most reliably be delivered, offering better value for taxpayers and improving the provision of ecosystem services from agricultural landscapes.

Accurate modelling of changes in freshwater supplies is critical in an era of increasing human demand, and changes in land use and climate. However, there are concerns that current landscape-scale models do not sufficiently capture catchment-level changes, whilst large-scale comparisons of empirical and simulated water yield changes are lacking. Here we modelled annual water yield in two time periods (1: 1985–1994 and 2: 2008–2017) across 81 catchments in England and validated against empirical data. Our objectives were to i) investigate whether modelling absolute or relative change in water yield is more accurate and ii) determine which predictors have the greatest impact on model accuracy. We used the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Annual Water Yield model. In this study, absolute values refer to volumetric units of million cubic metres per year (Mm3/y), either at the catchment or hectare level. Modelled annual yields showed high accuracy as indicated by the low Mean Absolute Deviation (MAD, based on normalised data, 0 is high and 1 is low accuracy) at the catchment (1: 0.013 ± 0.019, 2: 0.012 ± 0.020) and hectare scales (1: 0.03 ± 0.030, 2: 0.030 ± 0.025). But accuracy of modelled absolute change in water yield showed a more moderate fit on both the catchment (MAD = 0.055 ± 0.065) and hectare (MAD = 0.105 ± 0.089) scales. Relative change had lower accuracy (MAD = 0.189 ± 0.135). Anthropogenic modifications to the hydrological system, including water abstraction contributed significantly to the inaccuracy of change values at the catchment and hectare scales. Quantification of changes in freshwater provision can be more accurately articulated using absolute values rather than using relative values. Absolute values can provide clearer guidance for mitigation measures related to human consumption. Accuracy of modelled change is related to different aspects of human consumption, suggesting anthropogenic impacts are critically important to consider when modelling water yield.

Protected areas worldwide receive billions of visitors annually. The positive impact of nature on health and wellbeing, in addition to providing opportunities for cultural activities such as recreation and aesthetic appreciation, is well documented. Management to reduce negative impacts to biodiversity and conservation aims whilst providing amenities and access to visitors is important. Understanding environmental awareness of visitors and their on-site spatial patterns can assist in making effective management decisions within often constrained resources. However, there is a lack of strategies for site-specific identification and predictive mapping of visitors by environmental awareness. Here, we demonstrate a method to map on-site visitation by latent groups of visitors based on their environmental awareness of on-site issues. On-site surveys and participatory mapping were used to collect data on environmental awareness on bird nesting and spatial visitation patterns in an upland moor in northern England. Latent class analysis (LCA), a structural equation model, was used to discover underlying groups of environmental awareness, with random forest (RF) modelling, a machine learning technique, using a range of on-site predictors (bioclimatic, land cover, elevation, viewshed, and proximity to paths and freshwater) to predict and map visitation across the site by each group. Visitors were segmented into ‘aware’ and ‘ambiguous’ groups and their potential spatial visitation patterns mapped. Our results demonstrate the ability to uncover groups of users by environmental awareness and map their potential visitation across a site using a variety of on-site predictors. Spatial understanding of the movement patterns of differently environmentally aware groups of visitors can assist in efficient targeting of conservation education endeavours (i.e., signage, positioning of staff, monitoring programmes, etc.), therefore maximising their efficacy. Furthermore, we anticipate this method will be of importance to environmental managers and educators when deploying limited resources.

This article argues that the widespread notion that Israel lacks a Palestinian negotiating partner has endured in Israeli society despite the security community's unease with this narrative. Many of Israel's former generals and spymasters, some of whom were interviewed for this article, have argued that the "no partner" mantra runs counter to Israeli interests and that there are steps the government of Benjamin Netanyahu has avoided taking that would keep prospects for a two-state solution alive.

A clear majority of Israel’s retired high-ranking military and intelligence officials support the establishment of a separate Palestinian state.

For many years, Protected Areas (PA) have been an important tool for conserving nature. Recently, also societal aspects have been introduced into PA management via the introduction of the Ecosystem Services (ES) approach. This review discusses the historical background of PAs, PA management, and the ES approach. We then discuss the relevance and applicability of the ES approach for PA management, including the different definitions of ES, different classification methods, and the ways in which ES are measured. We conclude that there are still major challenges ahead in using the ES approach in PA management and so recommendations are given on the way in which the ES approach should be integrated into PA management.

Tropical disturbed forests play an important role in global carbon sequestration due to their rapid post-disturbance biomass accumulation rates. However, the accurate estimation of the carbon sequestration capacity of disturbed forests is still challenging due to large uncertainties in their spatial distribution. Using Google Earth Engine (GEE), we developed a novel approach to map cumulative disturbed forest areas based on the 27-year time-series of Landsat surface reflectance imagery. This approach integrates single date features with temporal characteristics from six time-series trajectories (two Landsat shortwave infrared bands and four vegetation indices) using a random forest machine learning classification algorithm. We demonstrated the feasibility of this method to map disturbed forests in three different forest ecoregions (seasonal, moist and dry forest) in Mato Grosso, Brazil, and found that the overall mapping accuracy was high, ranging from 81.3% for moist forest to 86.1% for seasonal forest. According to our classification, dry forest ecoregion experienced the most severe disturbances with 41% of forests being disturbed by 2010, followed by seasonal forest and moist forest ecoregions. We further separated disturbed forests into degraded old-growth forests and post-deforestation regrowth forests based on an existing post-deforestation land use map (TerraClass) and found that the area of degraded old-growth forests was up to 62% larger than the extent of post-deforestation regrowth forests, with 18% of old-growth forests actually being degraded. Application of this new classification approach to other tropical areas will provide a better constraint on the spatial extent of disturbed forest areas in Tropics and ultimately towards a better understanding of their importance in the global carbon cycle.

Despite wide adoption of pollution control technologies, industrial facilities emit about half the criteria air pollutants in the U.S. and contribute to poor air quality in many regions. Vegetation...

Hedgerows can provide a wide range of regulatory ecosystem services within improved grassland landscapes, such as soil function improvement, soil erosion reduction, biodiversity, water quality, and flood prevention and mitigation. Because of their beneficial effects, farmers are incentivised to retain their hedgerows and the planting of hedges has been encouraged in agri-environment schemes in Europe. Today, hedgerow planting it is one of the most popular practices adopted in the Countryside and Environmental Stewardships in England. The role of hedgerows in climate change mitigation has been increasingly recognized over the past decade, however, while other services have been more widely studies, less is known about hedges soil organic carbon (SOC) storage capacity. The Resilient Dairy Landscapes project aims at identifying strategies to reconcile dairy systems productivity and environment in the face of climate change, and with the Committee on Climate Change calling for a 30% - 40% increase in hedgerow length by 2050 in the UK, it is important to determine the role of hedgerows in meeting Net Zero targets. In this study, we estimate the extent of SOC stock beneath hedges and how it may vary with depth, hedge management and age, as well as how it may compare to SOC stock in adjacent agricultural fields. Thus, we measured SOC under 2-4 years old, 10 years old, 37 years old, and 40+ years old hedgerows at 10 cm intervals up to 50 cm of depth under 32 hedges located on dairy farms in Cumbria, UK. We found that the time since planting and the depth of samples play a crucial role in the amount of SOC stock stored underneath hedgerows when accounting for differences in soil type. Our results contribute measurable outcomes towards the estimate of targets for Net Zero 2050 and the extent of ecosystem services provision by hedgerow planting in agricultural landscapes.

Realising the carbon (C) sequestration capacity of agricultural soils is needed to reach Paris Climate Agreement goals; thus, quantifying hedgerow planting potential to offset anthropogenic CO

Cultural ecosystem services such as aesthetic value are highly context-specific and often present difficulties in their assessment. Here we present a case study in the northern English Protected Area of the Yorkshire Dales National Park. Utilising publicly available images, paired-comparison surveys, probability modelling, machine-learning based text annotations, natural language processing and regression analysis, we developed a spatial model to predict and map landscape aesthetics across the whole site. The predictive model found eighteen significant variables, including the positive role of rural areas, mountainous landforms and vegetation for aesthetic value. Finally, we demonstrate the potential of our approach to varying size datasets and partial paired-comparison matrices, finding a very good agreement with only 20% of paired comparisons. This study demonstrates the use of freely available data and mostly open source tools to ascertain landscape aesthetic value in a large Protected Area.

New renewable energy infrastructure is essential to deliver net zero policies in response to climate change, but a lack of community acceptance is a potential barrier. It is therefore important to understand what shapes community acceptance and identify policy responses. This paper presents a case study of community acceptance of a large-scale solar farm in the UK, the first to be classified as 'nationally significant' infrastructure. In doing so, it provides the first empirical study of community acceptance of a large-scale solar farm in a developed country context, building on existing studies which use hypothetical approaches such as choice experiments, or surveys which measure general attitudes rather than responses to specific developments. The paper uses mixed methods (quantitative content analysis of online comments on the planning proposal; qualitative semi-structured interviews with local residents and key stakeholders; and participant observation) to identify determinants shaping community acceptance of large-scale solar farms. We discover 28 determinants which we group into eight categories: aesthetic, environmental, economic, project details, temporal, social, construction and process. We argue that these findings help to reveal broader issues underlying community acceptance of solar farms and other renewable energy infrastructure: 'green-on-green' tensions; issues of scale and place attachment; policy, process and justice. We also contribute a novel understanding of community acceptance as 'relational', by which we mean it is informed by the deployment of other energy technologies and the wider energy policy landscape, not just the specific project. We conclude with recommendations for how policymakers can respond to the issues identified by this article.

The UK government has made formal commitments to reduce GHG emissions (e.g. under the Climate Change Act 2008 and subsequent amendments) and to protect/improve natural capital and the environment (e.g. as part of the 25 Year Environment Plan published in 2018). Meeting these objectives requires an integrated approach to two parallel challenges i) decarbonising the energy system and ii) better understanding and valuation of natural capital and ecosystem services. From an academic perspective this involves bringing together two substantial, but rather weakly connected bodies of research, while also acknowledging that this integration in a UK setting needs to recognise the international context (i.e. a whole systems perspective).The ADVENT project (ADdressing Valuation of Energy and Nature Together) has been funded by the UK National Environment Research Council to develop conceptual frameworks and modelling tools which ‘integrate the analysis of prospective UK energy pathways with considerations relating to the value of natural capital’. A methodology has been implemented to downscale the outputs of pathways from national energy system models and incorporate environmental impacts into the assessment of different options. This has required defining spatially-optimised distributions of investments in new energy infrastructure using a range of financial and welfare criteria. These distributions are then compared in terms of their construction, transport and land opportunity costs, as well as the implications for biodiversity, greenhouse gas emissions, recreation, visual amenity and water resources.This paper will present results from comparing different UK energy pathways through to 2050 in terms of the implications of electricity generation from three types of renewables (bioenergy, solar and onshore wind). The results illustrate that i) individual pathways can vary appreciably in their environmental impacts, ii) overall societal welfare can be enhanced by using spatial modelling to incorporate valuations of such impacts into implementation of pathways and iii) assessment outcomes can be sensitive to modelling assumptions (e.g. regarding the proportion of biomass feedstock from domestic or international sources). More broadly, the results demonstrate how important improvements can be achieved in the integration of environmental considerations into the assessment of future energy pathways at regional and national scales. The approach is now being further refined through the UK Energy Research Centre Phase 4 programme and ADVANCES Landscape Decisions project in the UK, as well as the five-country IRENES project funded by Interreg Europe.

Explicit inclusion of the role of ecosystems in life cycle assessment (LCA) is needed to prevent the selection of alternatives that depend on or degrade scarce ecosystem services (ES), and to help identify opportunities for enhancing sustainability by not just reducing impact but also protecting and restoring ecosystems and the diverse goods and services they supply. Various approaches have been suggested for including ES in LCA but a general computational framework is not yet available. This paper extends the framework of conventional process LCA to assess and encourage techno-ecological synergies in life cycle assessment (TES-LCA). It includes ecosystem modules along with process modules in LCA. Analogous to the technology matrix in conventional LCA, TES-LCA defines a “techno-ecological” matrix. It consists of four components: a technology matrix defined by economic flows, an intervention matrix interpreted as the ES demanded by technological activities, an ecosystem matrix interpreted as the capacity of ecosystems to supply these services, and a management matrix to capture the interaction between technological and ecological systems. This work demonstrates the computational structure through a toy example and discuss the major challenges of TES-LCA in terms of data availability for an exhaustive array of ES. This work suggests that such data need to be made available and included in future versions of life cycle inventory databases. The computational structure of TES-LCA is able to capture the interactions between and within technological and ecological systems. It enables including of the role and capacity of ecosystems in a life cycle. The framework can encourage development of data and models to enable practical use of TES-LCA, which can provide unique insights into absolute environmental sustainability by quantifying overshoots for specific ES, and help identify improvement strategies based on improving technological efficiency and restoring ecosystems.

Regionalized life cycle assessment (LCA) is receiving more attention among LCA practitioners due to spatial variation in process efficiency of technological systems and current status of ecological systems. However, the role of ecosystem services (ES) in supporting technological activities is still ignored. Techno-Ecological Synergies in Life Cycle Assessment (TES-LCA) is a methodology that captures the interactions between and within technological and ecological systems, along a product's life cycle. It accounts for local and absolute environmental sustainability by comparing the demand and supply of ES at multiple spatial scales. To facilitate its wider adoption, the basic computational framework has been proposed in Part 1, which includes technologies and ecosystems in an integrated manner. To handle the complications induced by explicitly considering ES, which operate within servicesheds at multiple spatial scales, the TES-LCA computational structure is modified to account for spatial variation in technological and ecological systems in Part 2. The regionalized TES-LCA framework is demonstrated through an expanded case study to show its capability to capture different scenarios of regionalization, including variation in process efficiency, ecological carrying capacity (CC), characterization factors (CF), and the scales at which ES operate. The approach is then proved to be general and able to subsume existing approaches, such as regionalized LCA, GIS-LCA and recent extensions based on normalizing CF by ecological CC to calculate absolute sustainability metrics. It is recommended that the developed computational structure should be implemented in LCA software with the functionality for handling geographical information. Also, the regionalized information about ES demand and supply needs to be made available in future versions of life cycle inventory databases.

Despite the proliferation of control technologies, air pollution remains a major concern across the United States, suggesting the need for a paradigm shift in methods for mitigating emissions. Based on data about annual emissions in U.S. counties and current land cover, we show that existing forest, grassland, and shrubland vegetation take up a significant portion of current U.S. emissions. Restoring land cover, where possible, to county-level average canopy cover can further remove pollution of SO2, PM10, PM2.5, and NO2 by an average of 27% through interception of particulate matter and absorption of gaseous pollutants. We find such nature-based solutions to be cheaper than technology for several National Emission Inventory sectors. Our results with and without monetary valuation of ecological cobenefits identify sectors and counties that are most economically attractive for nature-based solutions as compared to the use of pollution control technologies. We also estimate the sizes of urban and rural populations that would benefit from this novel ecosystem-based approach. This suggests that even though vegetation cannot fully negate the impact of emissions at all times, policies encouraging ecosystems as control measures in addition to technological solutions may promote large investments in ecological restoration and provide several societal benefits.