Search

Food production is at the heart of global sustainability challenges, with unsustainable practices being a major driver of biodiversity loss, emissions and land degradation. The concept of foodscapes, defined as the characteristics of food production along biophysical and socio-economic gradients, could be a way addressing those challenges. By identifying homologues foodscapes classes possible interventions and leverage points for more sustainable agriculture could be identified. Here we provide a globally consistent approximation of the world’s foodscape classes. We integrate global data on biophysical and socio-economic factors to identify a minimum set of emergent clusters and evaluate their characteristics, vulnerabilities and risks with regards to global change factors. Overall, we find food production globally to be highly concentrated in a few areas. Worryingly, we find particularly intensively cultivated or irrigated foodscape classes to be under considerable climatic and degradation risks. Our work can serve as baseline for global-scale zoning and gap analyses, while also revealing homologous areas for possible agricultural interventions.

Even in a world that limits global warming to below 2°C, government, consumer, and private sector responses to climate change, known as “climate transitions”, will materially change the financial future of the Brazilian cattle sector. Analysis of Brazil’s cattle sector shows that the risks posed by maintaining current production practices and business models are material for investors, producers and the sector’s entire value chain. Yet climate transitions also open significant new opportunities for producers and investors who stand to benefit from a projected 88 percent increase in agricultural investment by 2050.

Even in a world that limits global warming to below 2° C, government, consumer, and private sector responses to climate change, known as “climate transitions”, will materially change the financial future of the Brazilian soy sector. Soy stakeholders that invest in improving the sustainability and efficiency of their operations, reducing the emission intensity of production, exploring emerging market segments and diversifying revenue streams could see significant opportunities emerge as climate transitions benefit this commodity’s outlook.

We develop an econometric modelling framework to forecast commodity prices taking into account potentially different dynamics and linkages existing at different states of the world and using different performance measures to validate the predictions. We assess the extent to which the quality of the forecasts can be improved by entertaining different regime-dependent threshold models considering different threshold variables. We evaluate prediction quality using both loss minimization and profit maximization measures based on directional accuracy, directional value, the ability to predict turning points, and the returns implied by a simple trading strategy. Our analysis provides overwhelming evidence that allowing for regime-dependent dynamics leads to improvements in predictive ability for the Goldman Sachs Commodity Index, as well as for its five sub-indices (energy, industrial metals, precious metals, agriculture, and livestock). Our results suggest the existence of a trade-off between predictive ability based on loss and profit measures, which implies that the particular aim of the prediction exercise carried out plays a very important role in terms of defining which set of models is the best to use.

Mineral nitrogen (N) fertilizer use is essential to maintain high-yielding cropping systems that presently provide food for nearly half of humanity. Simultaneously, it causes a range of detrimental impacts such as greenhouse gas emissions, eutrophication, and contamination of drinking water. There is growing recognition of the need to balance crop production with the impacts of fertilizer use. Here we provide a global assessment of the potential to reduce mineral fertilizer use through four interventions: capping surpluses, enhancing manure cycling to cropland, cultivation of off-season green manures, and cycling of human excreted N to cropland. We find that the combined potential of these interventions is a reduction in global N fertilizer use by 21-52%. The availability of interventions is spatially heterogeneous with most cropland having three to four interventions available with alternative N sources tending to be more abundant on cropland already receiving fertilizer. Our assessment highlights that these locally in part already practiced interventions bear great opportunities to mitigate synthetic N use and dependency globally. Yet, their limited adoption underpins the need for cross-sectoral policies to overcome barriers to their implementation and agronomic research on their robust scaling.

Based on an extensive model intercomparison, we assessed trends in biodiversity and ecosystem services from historical reconstructions and future scenarios of land-use and climate change. During the 20th century, biodiversity declined globally by 2 to 11%, as estimated by a range of indicators. Provisioning ecosystem services increased several fold, and regulating services decreased moderately. Going forward, policies toward sustainability have the potential to slow biodiversity loss resulting from land-use change and the demand for provisioning services while reducing or reversing declines in regulating services. However, negative impacts on biodiversity due to climate change appear poised to increase, particularly in the higher-emissions scenarios. Our assessment identifies remaining modeling uncertainties but also robustly shows that renewed policy efforts are needed to meet the goals of the Convention on Biological Diversity.

Item does not contain fulltext

Eradicating extreme poverty everywhere by 2030 has proved to be challenging. Uplifting millions out of poverty might lead to exceeding the Earth’s environmental boundaries. Using a global integrated assessment model, we assess the effectiveness of 900 strategies under 25 socioeconomic settings in eliminating poverty and quantify their impacts on the Earth system by 2050. Our reference scenario, which follows a post-pandemic economic trend with an annual economic growth rate of 2.05%, projects an extreme poverty rate of 7.34% (uncertainty range 6.29%–8.73%) in 2030. Even under optimistic settings, it may take over two decades to eradicate extreme poverty. Focusing more on environmental drivers of poverty and following historical trends in fiscal policies and social safety nets, we identified two robust strategies characterized by ambitious educational attendance, sustainable dietary choices, low fossil fuel consumption and energy demand, and low fertilizer use, which offset negative environmental effects without compromising the poverty eradication gains.

The spatio-temporal distribution, flow and end use of phosphorus (P) embedded in traded agricultural products are poorly understood. Here we use global trade matrices to analyse the partial factor productivity of P (output per unit of P input) for crop and livestock products in 200 countries and their cumulative contributions to the export or import of agricultural products over 1961-2019. In these six decades, the trade of agricultural P products has increased global partial factor productivity for crop and livestock production and has theoretically saved 67 Tg P in fertilizers and 1.6 Tg P in feed. However, trade is now at risk of contributing to wasteful use of P resources globally due to a decline in trade optimality, as agricultural products are increasingly exported from low to high partial factor productivity countries and due to P embedded in imported agricultural products mainly lost to the environment without recycling. Integrated crop-livestock production systems and P-recycling technologies can help.

04 juli 2023, Contains fulltext : 292089.pdf (Publisher’s version ) (Closed access)

Most of the world's nations (around 130) have committed to reaching net-zero carbon dioxide or greenhouse gas (GHG) emissions by 2050, yet robust policies rarely underpin these ambitions. To investigate whether existing and expected national policies will allow Brazil to meet its net-zero GHG emissions pledge by 2050, we applied a detailed regional integrated assessment modelling approach. This included quantifying the role of nature-based solutions, such as the protection and restoration of ecosystems, and engineered solutions, such as bioenergy with carbon capture and storage. Our results highlight ecosystem protection as the most critical cost-effective climate mitigation measure for Brazil, whereas relying heavily on costly and not-mature-yet engineered solutions will jeopardise Brazil's chances of achieving its net-zero pledge by mid-century. We show that the full implementation of Brazil's Forest Code (FC), a key policy for emission reduction in Brazil, would be enough for the country to achieve its short-term climate targets up to 2030. However, it would reduce the gap to net-zero GHG emissions by 38% by 2050. The FC, combined with zero legal deforestation and additional large-scale ecosystem restoration, would reduce this gap by 62% by mid-century, keeping Brazil on a clear path towards net-zero GHG emissions by around 2040. While some level of deployment of negative emissions technologies will be needed for Brazil to achieve and sustain its net-zero pledge, we show that the more mitigation measures from the land-use sector, the less costly engineered solutions from the energy sector will be required. Our analysis underlines the urgent need for Brazil to go beyond existing policies to help fight climate emergency, to align its short- and long-term climate targets, and to build climate resilience while curbing biodiversity loss.

According to most climate mitigation scenario assessments, limiting global warming to 1.5–2°C in the long run will not be possible without the extensive deployment of carbon dioxide removal (CDR) from the atmosphere. CDR is required for drawing down and achieving net-zero CO2 emissions by mid-century. Thereafter, CO2 removals will likely need to exceed residual CO2 emissions, resulting in net negative emissions. A policy framework based on ‘carbon removal obligations’ (CROs) has been proposed to respond to concerns about the financial and fiscal viability, the lack of incentives for CDR uptake, as well as the physical and technological risks associated with any climate mitigation scenario that relies on large scale CDR. Here we propose an updated and improved CRO policy framework, consisting of two core elements: the ‘principal CRO mechanism’ obliges emitters of a tonne of CO2 to remove a tonne of CO2 at the time of maturity of the CRO. On top of this obligation, CRO holders need to pay a fee for the temporary storage of CO2 in the atmosphere. This ‘CRO pricing instrument’ is used by regulators to steer the carbon emissions and removals pathways independently. Our update suggests that markets for CDR under the CRO framework should operate independently from markets for emission reductions. We propose a blueprint for legal implementation where CROs are integrated akin to private financial borrowing and debt mechanisms. By aligning CROs with established financial systems, we leverage familiar institutional roles, seamlessly integrating climate mitigation into the core economy.

The "Hotelling rule" (HR) called to be "the fundamental principle of the economics of exhaustible resources" has a logical deficiency which was never paid a sufficient attention to. This deficiency should be taken into account before attempting to explain discrepancies between the price prediction provided by the HR and historically observed prices. Our analysis is focused on the HR in its original form, we do not touch upon other aspects such as varying extraction costs and other ways of upgrading the original model underlying the Hotelling rule. We conclude that HR can not be derived from the simple models as it was claimed, therefore it should be understood as an assumption on its own, and not as a rule derived from other more basic assumptions.

Brazil once set the example for curtailing deforestation with command and control policies, but, in the last decade, these interventions have gone astray. Environmental research and policy today are largely informed by the earlier successes of deforestation interventions, but not their recent failures. Here, we investigate the resilience of deforestation interventions. We discuss how the recent trend reversal in Brazil came to be, and what its implications for the design of future policies are. We use newly compiled information on environmental fines in an econometric model to show that the enforcement of environmental policy has become ineffective in recent years. Our results add empirical evidence to earlier studies documenting the erosion of the institutions responsible for forest protection, and highlight the considerable deforestation impacts of this erosion. Future efforts for sustainable forest protection should be aimed at strengthening institutions, spreading responsibilities, and redistributing the common value of forests via incentive-based systems.

Results of the Scenathon 2019.

The agricultural costing model is a cost calculator that includes national-scale information on costs of production for ten major crops including, barley, groundnut, maize, potato, rice, sorghum, soybean, sugar beet, sunflower, and wheat, and works in combination to FABLE calculator. The present version of the costing inventory includes five cost items corresponding to costs for the production inputs of fertiliser, pesticides, labour, machinery and fuel. The current setting of the cost calculation is based on the integration of physical requirement per hectare for any given production input, the respective cost per unit of input and the cropland extent.

The achievement of several sustainable development goals and the Paris Climate Agreement depends on rapid progress towards sustainable food and land systems in all countries. We have built a flexible, collaborative modeling framework to foster the development of national pathways by local research teams and their integration up to global scale. Local researchers independently customize national models to explore mid-century pathways of the food and land use system transformation in collaboration with stakeholders. An online platform connects the national models, iteratively balances global exports and imports, and aggregates results to the global level. Our results show that actions toward greater sustainability in countries could sum up to 1 Mha net forest gain per year, 950 Mha net gain in the land where natural processes predominate, and an increased CO2 sink of 3.7 GtCO2e yr−1 over the period 2020–2050 compared to current trends, while average food consumption per capita remains above the adequate food requirements in all countries. We show examples of how the global linkage impacts national results and how different assumptions in national pathways impact global results. This modeling setup acknowledges the broad heterogeneity of socio-ecological contexts and the fact that people who live in these different contexts should be empowered to design the future they want. But it also demonstrates to local decision-makers the interconnectedness of our food and land use system and the urgent need for more collaboration to converge local and global priorities.

Global net land carbon uptake or net biome production (NBP) has increased during recent decades1. Whether its temporal variability and autocorrelation have changed during this period, however, remains elusive, even though an increase in both could indicate an increased potential for a destabilized carbon sink2,3. Here, we investigate the trends and controls of net terrestrial carbon uptake and its temporal variability and autocorrelation from 1981 to 2018 using two atmospheric-inversion models, the amplitude of the seasonal cycle of atmospheric CO2 concentration derived from nine monitoring stations distributed across the Pacific Ocean and dynamic global vegetation models. We find that annual NBP and its interdecadal variability increased globally whereas temporal autocorrelation decreased. We observe a separation of regions characterized by increasingly variable NBP, associated with warm regions and increasingly variable temperatures, lower and weaker positive trends in NBP and regions where NBP became stronger and less variable. Plant species richness presented a concave-down parabolic spatial relationship with NBP and its variability at the global scale whereas nitrogen deposition generally increased NBP. Increasing temperature and its increasing variability appear as the most important drivers of declining and increasingly variable NBP. Our results show increasing variability of NBP regionally that can be mostly attributed to climate change and that may point to destabilization of the coupled carbon–climate system.

This report delves into the multifaceted dimensions of carbon dioxide removal methods. The report discusses the role of carbon dioxide removal methods in contributing to attaining the long-term goal of the Paris Agreement and investigates best practices in the implementation of the collaborative instruments under Article 6 for their incentivisation and scaling. The present climate policy and actual decision-making are still centred on achieving net-zero carbon emissions but the long-term challenge is the inevitable reversal of the overshoot, requiring carbon removal to outpace residual emissions, leading to net negative emissions globally. The report discusses the need to assign responsibility for climate overshoot reversal in order to guarantee the viability of a global net-negative GHG economy. The report analyses and proposes ways to address risks associated with carbon removal, including mitigation deterrence, that carbon removed from the atmosphere might be re-released, carbon-leakage effects, and challenges related to monitoring mitigation outcomes. It offers recommendations based on these deliberations.

22 september 2022, Contains fulltext : 253761.pdf (Publisher’s version ) (Closed access)

07 juli 2022, Contains fulltext : 251985.pdf (Publisher’s version ) (Open Access)

Shadow prices are well understood and are widely used in economic applications. However, there are limits to where shadow prices can be applied assuming their natural interpretation and the fact that they reflect the first order optimality conditions (FOC). In this paper, we present a simple ad-hoc example demonstrating that marginal cost associated with exercising an optimal control may exceed the respective cost estimated from a ratio of shadow prices. Moreover, such cost estimation through shadow prices is arbitrary and depends on a particular (mathematically equivalent) formulation of the optimization problem. These facts render a ratio of shadow prices irrelevant to estimation of optimal marginal cost. The provided illustrative optimization problem links to a similar approach of calculating social cost of carbon (SCC) in the widely used dynamic integrated model of climate and the economy (DICE).

Progress to date toward the Sustainable Development Goals (SDGs) has fallen short of expectations and is unlikely to fully meet 2030 targets. Past assessments have mostly focused on short- and medium-term evaluations, thus limiting the ability to explore the longer-term effects of systemic interactions with time lags and delay. Here we undertake global systems modeling with a longer-term view than previous assessments in order to explore the drivers of sustainability progress and how they could play out by 2030, 2050, and 2100 under different development pathways and quantitative targets. We find that early planning for systems change to shift from business as usual to more sustainable pathways is important for accelerating progress toward increasingly ambitious targets by 2030, 2050, and 2100. These findings indicate the importance of adopting longer-term timeframes and pathways to ensure that the necessary pre-conditions are in place for sustainability beyond the current 2030 Agenda.

The social cost of carbon (SCC) is estimated by integrated assessment models (IAMs) and is widely used by government agencies to value climate policy impacts. Although there is an ongoing debate about obtained numerical estimates and related uncertainties, little attention has been paid so far to the SCC calculation method itself. This work attempts to fill the gap by providing the theoretical background and economic interpretation of the SCC calculation approach implemented in the DICE (Dynamic Integrated Climate-Economy) IAM. Our analysis indicates that the present calculation method is unable to reflect the linkages between two key IAM components—complex interconnected systems—climate and economy, both influenced by emission abatement policies. Within the modeling framework of DICE, the presently estimated SCC valuates emissions, which are beyond policy control, against consumption of products, which cannot be produced by the economy. This makes the SCC irrelevant for application in climate-economic policies and, therefore, calls for a replacement by a more appropriate indicator. An apparent SCC alternative, which can be considered for policy formulation, is the direct output of the DICE model, the socially optimal marginal abatement cost (SMAC), which corresponds to technological possibilities at the optimal level of carbon emissions abatement. In policymaking, because of the revealed SCC deficiency, great attention needs to be paid to the use of estimates obtained earlier.

There is an urgent need for countries to transition their national food and land-use systems toward food and nutritional security, climate stability, and environmental integrity. How can countries satisfy their demands while jointly delivering the required transformative change to achieve global sustainability targets? Here, we present a collaborative approach developed with the FABLE—Food, Agriculture, Biodiversity, Land, and Energy—Consortium to reconcile both global and national elements for developing national food and land-use system pathways. This approach includes three key features: (1) global targets, (2) country-driven multi-objective pathways, and (3) multiple iterations of pathway refinement informed by both national and international impacts. This approach strengthens policy coherence and highlights where greater national and international ambition is needed to achieve global goals (e.g., the SDGs). We discuss how this could be used to support future climate and biodiversity negotiations and what further developments would be needed.

Ending poverty in all its forms everywhere is the first global goal of the United Nations 2030 Agenda for Sustainable Development. Poverty eradication is a long-term process that faces many uncertainties and complex interactions with other Sustainable Development Goals (SDGs). In order to better understand poverty and contribute to addressing poverty and sustainability, this paper aims to conduct a systematic review of model-based poverty scenario analyses in the context of the SDGs. We first review 144 studies in terms of bibliometrics (i.e., publication types, research topics for poverty, research objects, research scales and geographic locations) and the characteristics and scope of the models and analyses (i.e., model types, purposes, states, temporal and spatial range, sectors considered, poverty and other SDGs indicators). Second, we discuss the pros and cons of different types of models and identify seven representative models. We also discuss the synergies and trade-offs between poverty and other SDGs. Finally, we identify four potential research gaps in model-based poverty scenario analysis and provide suggestions for future research. The review shows that poverty scenario analysis was carried out mainly from a single perspective, such as economic, ecological, and agricultural. Few studies used effective models to analyze poverty in an integrated analysis of interactions between multiple sectors. Comprehensive multi-sector models are needed for global and regional poverty scenario analysis over the medium- and long-term to enhance the understanding of combined effects, synergies, and trade-offs between poverty and other SDGs.

The development of detailed national pathways towards sustainable food and land systems aims to provide stakeholders with clarity on how long-term goals could be achieved and to reduce roadblocks in the way to making commitments. However, the inability to perfectly capture the relationships between all variables in a system and the unknown probability of future values (deep uncertainty) makes it very difficult to design scenarios that account for the full breadth of system uncertainty. Here we use scenario discovery to systematically explore the effect of different parameter ranges on model outputs, and design resilient pathways to sustainability in which multiple target achievement requires a broad portfolio of solutions. We use a model of the Australian food and land system, the FABLE (Food, Agriculture, Biodiversity, Land-use, Energy) Calculator, to investigate conditions for achieving a sustainable Australian food and land system under scenarios based on the Shared Socioeconomic Pathways (SSP) 1, 2, and 3 narratives. Here we link the FABLE Calculator with a Monte Carlo simulation tool to explore hundreds of thousands of scenarios. This allows us to identify the ranges of systemic drivers that achieve multiple sustainability targets around diets, net forest growth, agricultural water consumption, greenhouse gas emissions, biodiversity conservation, and exports by 2050. Our results show that livestock productivity and density, afforestation, and dietary change are powerful influencers for sustainability target achievement. Around 10% of the SSP1 scenarios could achieve all modelled sustainability targets. However, practically none of the scenarios based on SSP2 and SSP3 narratives could achieve such targets. The results suggest that there are options to achieve a more sustainable and resilient Australian food and land-use system with better socio-economic and environmental outcomes than under current trends. However, its achievement requires significant structural

Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki (https://www.geo-wiki.org/). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services.

As the COVID-19 virus spread over the world, governments restricted mobility to slow transmission. Public health measures had different intensities across European countries but all had significant impact on people's daily lives and economic activities, causing a drop of CO2 emissions of about 10% for the whole year 2020. Here, we analyze changes in natural gas use in the industry and gas distribution to the built environment during the first half of year 2020 with daily gas flows data from pipeline and storage facilities in Europe. We find that reductions of industrial gas use reflect decreases in industrial production across most countries. Surprisingly, natural gas use in the built environment also decreased despite most people being confined at home and cold spells in March 2020. Those reductions that we attribute to the impacts of COVID-19 remain of comparable magnitude to previous variations induced by cold or warm climate anomalies in the cold season. We conclude that climate variations played a larger role than COVID-19 induced stay-home orders in natural gas consumption across Europe.

In the FABLE scenathons, trade is the element that connects all countries and Rest Of the World (ROW) models. By maintaining consistent national trade assumptions, we can secure that national pathways and global targets are consistent at the global level. This report describes the advances on a method based on Reinforcement Learning (RL) and a set of tools to support collective decision-making during the Scenathon process. The aim is to collectively address four global goals while respecting national priorities and pathways and keeping global consistency. The developed tools are The Smart Linker Tool (SLT), the Scenathon Lab distributed platform and the visualization tool to assess the training and optimization processes of the SLT. within this framework we developed a case of study based on the beef and soybean trade behaviour of the FABLE Scenathon 2019, integrating the FABLE calculators of Argentina, Australia, Brazil, China and the US. The architecture and first results are described.

Phosphorus (P) is an essential nutrient to boost crop yields, but P runoff can cause nutrient over-enrichment in agricultural watersheds and can lead to irreversible effects on aquatic ecosystems and their biodiversity. Lake Erie is one prominent example as this watershed has experienced multiple episodes of harmful algal blooms over the last decades. Annual P loads crucially depend on yearly weather variations, which can create the risk of years with high runoff and excessive nutrient loads. Here we apply stochastic modeling to derive sustainable management strategies that balance crop yield optimization with environmental protection, while accounting for weather variability as well as weather trends as a result of climate change. We demonstrate that ignoring annual weather variations results in mitigation efforts for environmental pollution that are largely insufficient. Accounting explicitly for future variations in precipitation allows us to control the risk of emissions exceeding the P target loads. When realistic risk targets are imposed, we find that a package of additional measures is required to avoid P over-enrichment in the Lake Erie watershed. This package consists of a substantial reduction of P inputs (approximately 30 for different accepted risk levels), adoption of cover crops throughout the near- and mid-century, and cultivation of less nutrient-intensive crops (30 more soy at the expense of corn). Although climate change reinforces these conclusions, we find that the accepted risk level of exceeding P target loads is the predominant factor in defining a sustainable nutrient management policy.

Traditional integrated modeling (IM) is based on developing and aggregating all relevant (sub)models and data into a single integrated linear programming (LP) model. Unfortunately, this approach is not applicable for IM under asymmetric information (ASI), i.e., when “private” information regarding sectoral/regional models is not available, or it cannot be shared by modeling teams (sectoral agencies). The lack of common information about LP submodels makes LP methods inapplicable for integrated LP modeling. The aim of this paper is to develop a new approach to link and optimize distributed sectoral/regional optimization models, providing a means of decentralized cross-sectoral coordination in the situation of ASI. Thus, the linkage methodology enables the investigation of policies in interdependent systems in a “decentralized” fashion. For linkage, the sectoral/regional models do not need recoding or reprogramming. They also do not require additional data harmonization tasks. Instead, they solve their LP submodels independently and in parallel by a specific iterative subgradient algorithm for nonsmooth optimization. The submodels continue to be the same separate LP models. A social planner (regulatory agency) only needs to adjust the joint resource constraints to simple subgradient changes calculated by the algorithm. The approach enables more stable and resilient systems’ performance and resource allocation as compared to the independent policies designed by separate models without accounting for interdependencies. The paper illustrates the application of the methodology to link detailed energy and agricultural production planning models under joint constraints on water and land use.