The agricultural expansion and intensification required to meet growing food and agri-based product demand present important challenges to future levels and management of biodiversity and ecosystem services. Influential actors such as corporations, governments, and multilateral organizations have made commitments to meeting future agricultural demand sustainably and preserving critical ecosystems. Current approaches to predicting the impacts of agricultural expansion involve calculation of total land conversion and assessment of the impacts on biodiversity or ecosystem services on a per-area basis, generally assuming a linear relationship between impact and land area. However, the impacts of continuing land development are often not linear and can vary considerably with spatial configuration. We demonstrate what could be gained by spatially explicit analysis of agricultural expansion at a large scale compared with the simple measure of total area converted, with a focus on the impacts on biodiversity and carbon storage. Using simple modeling approaches for two regions of Brazil, we find that for the same amount of land conversion, the declines in biodiversity and carbon storage can vary two-to fourfold depending on the spatial pattern of conversion. Impacts increase most rapidly in the earliest stages of agricultural expansion and are more pronounced in scenarios where conversion occurs in forest interiors compared with expansion into forests from their edges. This study reveals the importance of spatially explicit information in the assessment of land-use change impacts and for future land management and conservation.ecosystem services | deforestation | agricultural expansion | fragmentation | edge effects

This paper reviews the many criticisms that Integrated Assessment Models (IAMs)—the bedrock of mitigation analysis—have received in recent years. Critics have asserted that there is a lack of transparency around model structures and input assumptions, a lack of credibility in those input assumptions that are made visible, an over-reliance on particular technologies and an inadequate representation of real-world policies and processes such as innovation and behaviour change. The paper then reviews the proposals and actions that follow from these criticisms, which fall into three broad categories: scrap the models and use other techniques to set out low-carbon futures; transform them by improving their representation of real-world processes and their transparency; and supplement them with other models and approaches. The article considers the implications of each proposal, through the particular lens of how it would explore the role of a key low-carbon technology—bioenergy with carbon capture and storage (BECCS), to produce net negative emissions. The paper concludes that IAMs remain critically important in mitigation pathways analysis, because they can encompass a large number of technologies and policies in a consistent framework, but that they should increasingly be supplemented with other models and analytical approaches.

Integrated assessment models (IAMs) have emerged as key tools for building and assessing long term climate mitigation scenarios. Due to their central role in the recent IPCC assessments, and international climate policy analyses more generally, and the high uncertainties related to future projections, IAMs have been critically assessed by scholars from different fields receiving various critiques ranging from adequacy of their methods to how their results are used and communicated. Although IAMs are conceptually diverse and evolved in very different directions, they tend to be criticised under the umbrella of ‘IAMs’. Here we first briefly summarise the IAM landscape and how models differ from each other. We then proceed to discuss six prominent critiques emerging from the recent literature, reflect and respond to them in the light of IAM diversity and ongoing work and suggest ways forward. The six critiques relate to (a) representation of heterogeneous actors in the models, (b) modelling of technology diffusion and dynamics, (c) representation of capital markets, (d) energy-economy feedbacks, (e) policy scenarios, and (f) interpretation and use of model results.

Low carbon options for the chemical industry include switching from fossil to renewable energy, adopting new low-carbon production processes, along with retrofitting current plants with carbon capture for ulterior use (CCU technologies) or storage (CCS). In this paper, we combine a dynamic Life Cycle Assessment (d-LCA) with economic analysis to explore a potential transition to low-carbon manufacture of formic acid. We propose new methods to enable early technical, environmental and economic assessment of formic acid manufacture by electrochemical reduction of CO2 (CCU), and compare this production route to the conventional synthesis pathways and to storing CO2 in geological storage (CCS). Both CCU and CCS reduce carbon emissions in particular scenarios, although the uncertainty in results suggests that further research and scale-up validation are needed to clarify the relative emission reduction compared to conventional process pathways. There are trade-offs between resource security, cost and emissions between CCU and CCS systems. As expected, the CCS technology yields greater reductions in CO2 emissions than the CCU scenarios and the conventional processes. However, compared to CCS systems, CCU has better economic potential and lower fossil consumption, especially when powered by renewable electricity. The integration of renewable energy in the chemical industry has an important climate mitigation role, especially for processes with high electrical and thermal energy demands. of CO2 for CCS and/or CCU applications in the future, if the power sector was to become 1 fully decarbonised (Mathy et al. 2018;McDowall et al. 2018). Furthermore, CCU could 2 be critical in the near-term to support the development of early CCS infrastructure. In this 3 overall context, a debate has arisen around the relative benefits of CCU and CCS, 4 introducing divergent perspectives about the role of CO2 utilization compared to storage 5 in mitigating climate change. Some studies have chosen to group them as carbon capture 6 and storage or utilisation (CCUS) (BEIS, 2018).

Summary Food is needed to maintain our physical integrity and therefore meets a most basic human need. The food sector got in the focus of environmental policy, because of its environmental implications and its inefficiency in terms of the amount of food lost along the value chain. The European Commission (EC) flagged the food waste issue a few years ago and adopted since then a series of policies that partially address the problem. Among these, the Resource Efficiency Roadmap set the aspirational goal of reducing the resource inputs in the food chain by 20% and halving the disposal of edible food waste by 2020. Focusing on consumer food waste, we tested what a reduction following the Roadmap's food waste target would imply for four environmental categories in EU28 (European Union 28 Member States): greenhouse gas emissions, land use, blue water consumption, and material use. Compared to the 2011 levels, reaching the target would lead to 2% to 7% reductions of the total footprint depending on the environmental category. This equals a 10% to 11% decrease in inputs in the food value chain (i.e., around half of the resource use reductions targeted). The vast majority of potential gains are related to households, rather than the food‐related services. Most likely, the 2020 target will not be met, since there is insufficient action both at Member State and European levels. The Sustainable Development Goals provide a new milestone for reducing edible food waste, but Europe needs to rise up to the challenge of decreasing its per capita food waste generation by 50% by 2030.

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CO 2 emissions induced by human activities are the major cause of climate change; hence, strong environmental policy that limits the growing dependence on fossil fuel is indispensable. Tradable permits and environmental taxes are the usual tools used in CO 2 reduction strategies. Such economic tools provide incentives to polluting industries to reduce their emissions through market signals. The aim of this work is to investigate the direct and indirect effects of an environmental tax on Spanish products and services. We apply an environmentally extended input-output (EIO) model to identify CO 2 emission intensities of products and services and, accordingly, we estimate the tax proportional to these intensities. The short-term price effects are analyzed using an input-output price model. The effect of tax introduction on consumption prices and its influence on consumers' welfare are determined. We also quantify the environmental impacts of such taxation in terms of the reduction in CO 2 emissions. The results, based on the Spanish economy for the year 2007, show that sectors with relatively poor environmental profile are subjected to high environmental tax rates. And consequently, applying a CO 2 tax on these sectors, increases production prices and induces a slight increase in consumer price index and a decrease in private welfare. The revenue from the tax could be used to counter balance the negative effects on social welfare and also to stimulate the increase 2 of renewable energy shares in the most impacting sectors. Finally, our analysis highlights that the environmental and economic goals cannot be met at the same time with the environmental taxation and this shows the necessity of finding other (complementary or alternative) measures to ensure both the economic and ecological efficiencies.

The performance of agricultural systems and their environmental impacts can vary considerably within a single crop supply chain, due to differences in farming practices, soil properties, and yearly climatic conditions. In this paper, we characterised the variability of carbon footprints of open-field tomato production by analysing a comprehensive farm dataset gathered over 4 years. We also assessed the importance of the different drivers of variability as compared to model uncertainties. The primary data used in this study were collected from 189 farms from the Extremadura region in Spain and Portugal over a period of four years, from 2012 to 2015. We modelled the carbon footprint of these farms using the Cool Farm Tool model developed by Hillier et al. (2011) and conducted statistical analysis on the results to understand the relative importance of inter-year and intra-year variability. We performed sensitivity analysis to understand how sensitive the results were to variability in the farmers' input parameters and to the uncertainty in model parameters. This was done by varying all factors oneat-a-time, and then by running a Monte Carlo simulation where all uncertainties were propagated simultaneously. Results clearly show significant inter-year and intra-year variability in carbon footprints of tomato production within the study region. We observed approximately 20% variation for each annual carbon footprint (intra-year variability), resulting in an overall 28% coefficient of variation in the aggregated footprint across the different years. The carbon footprint of the whole tomato supply, calculated with the 4-year dataset, showed a weighted geometric mean of 51 kg CO2-eq/t and a weighted GSD of 1.32, meaning a 95% confidence interval of 29 to 89 kg CO2-eq/t. Results also show that small farms were characterised by a larger variability than larger ones. This highlights the need to weight farm results by production volumes if the objective is to obtain a carbon footprint for the total production in a given region. The carbon footprint was found to be mainly sensitive to variability in farm practices, notably extent of pump irrigation and choice and amount of fertiliser used, with model uncertainties influencing the results to a relatively smaller extent. Further work is needed to extend these findings to other crops, regions and impact categories.