In global scenarios of 2 degrees C and 1.5 degrees C warming, integrated assessment model (IAM) studies' results demonstrated that bioenergy coupled with carbon capture and storage (BECCS) can help achieve net zero and net negative emissions within the course of this century by removing CO2 emissions from the atmosphere at scale while producing energy. However, the feasibility of BECCS depends on the availability of biomass resource, sizeable demand to cover, and access to long-term CO2 storage that differs from one region to another. Several studies have investigated the BECCS deployment scenarios at national levels. However, most studies have not addressed the implication of BECCS supply chain design in the context of long-term regional electricity system development, considering the regional disparity of energy resource, CO2 sources, and CO2 sinks. This study assessed the potential and impacts of BECCS development in Indonesia's electricity sector net zero transition scenarios using capacity expansion model that incorporates grid expansion problem considering the regional disparities of resource and existing infrastructure. In the most stringent emission scenario, 16 GW of BECCS power generation capacities are deployed with a potential emission reduction of 265 Mt CO2, which requires about 1877 GJ of biomass and expansion of 55,000 Mt-km CO2 transport pipeline capacity. Model results have demonstrated the role of BECCS as "backstop" technology for decarbonization, considering the high return on emission reduction per dollar spent rather than electricity generation. Scenarios leading to large negative emissions from BECCS will require additional biomass resources larger than the existing domestic supply, which can risk increasing negative impacts toward natural resources and environment.

Tropical landscapes in Southeast Asia are susceptible to the detrimental consequences of fires. However, investigations on ignition sources in the region are limited despite its importance in formulating prevention strategies. We addressed this gap using a systems approach: a graph-theoretic method to identify the spatial origins of the devastating 2015 equatorial Southeast Asian fires. We then fitted a Random Forest model to explain the suitable conditions of fire origins using the identified origin points (n = 74 539-74 950) and predictors that comprised climatic, biophysical, and anthropogenic factors. We found that most fires in the region (similar to 84%) have multiple (>1) origin points. Overall, our modelling results conform to the anthropogenic origins of fires in the region, though geographical and climatic variables, such as ecoregion and long-term average vapour pressure deficit, act as major predictors of fire origins. The high importance of ecoregions in our model calls for ecoregion-based fire mitigation approaches. Incorporating ecoregion in our analyses has allowed for the identification of previously overlooked areas with high fire origin likelihood and areas potentially undergoing a major increase in fire origin likelihood following likely anthropogenic landscape modifications. Our identification of multi-origin fires in the region emphasises that preventing the expansion of areas with high fire origin likelihood is our best chance of preventing such resilient hazards.

Capacity expansion optimisation is a widely used techno-economic analysis particularly on topics related to climate change mitigation and renewable energy transition. Using optimisation models to investigate capacity expansion in regions that potentially require significant grid infrastructure development requires incorporation of grid expansion problem within the optimisation. This study presents the development of SELARU, a spatially explicit optimisation model that incorporates the economies of scale of grid expansion using contextualized geographical feature to form the model's high-resolution spatial units. The model is used to investigate the case study of Indonesia using various spatial treatments to demonstrate the impact of detailed spatial depiction of grid expansion. Results reveal significant difference in renewable energy deployment trajectory (up to 2272 % increase in new generation capacity) between high-resolution spatial depiction of grid expansion vis-a`-vis non spatially explicit energy system optimisation. Due to its high-resolution, SELARU also generates detailed information on the geographical extent of grid expansion requirement, which provides more realistic insights on governance challenges of renewable energy transition. Careful consideration of spatial representation is crucial when optimisation model is used to evaluate scenarios that concern technology selection such as renewable energy deployment or climate change mitigation.

In the effort to align with Paris goals, decision-makers set targets that usually concern milestones earlier than 2100. These targets can be derived from different considerations of long-term implications of actions. This study investigates the implications of deciding on emissions targets based on myopic vis-à-vis perfect foresight using long-term energy system optimization model. The study reveals cost discrepancies correspond to the gaps between emissions derived from mixed integer linear programming (MILP) solution in perfect foresight scenarios versus exogenous values in myopic scenarios. When considering myopic approach, our study suggests that avoiding drastic emissions reduction can deliver minimum cost discrepancies relative to what can be achieved with perfect foresight. However, this poses a dilemma where less drastic emissions targets may risk increasing fossil power generation under lenient emissions reduction targets. Complementing less drastic emissions reduction targets with more ambitious policies promoting renewables is necessary to avoid the risk of increased reliance on fossil power generation.

Abstract Non-Technical Summary Information on social aspects of climate change intervention, such as behavioral choices and public acceptance, are often not included in global climate models. As a result, they have been critiqued for not adequately reflecting ‘real world’ conditions. At the same time, these models are important and influential policy tools. To improve these models, calls are being made for more interaction – or integration – between the social science and modelling research communities. Yet, it remains unclear how to achieve this. Responding to this gap, we explore what kind of integration is currently taking place, how, and opportunities for further development. Technical Summary The importance of social drivers of climate change interventions, or social aspects, is currently underrepresented in computational modelling projections. These parameters are largely excluded from estimates of technical mitigation potential, feasibility, and tools like integrated assessment models (IAMs) and other large-scale models that influence the development of climate policies and notable bodies like the Intergovernmental Panel on Climate Change. This paper contributes to calls being made within the research community to address this gap and strengthen linkages between modelling practices and social science insights. Using nature-based solutions (NbS) as a framing, we present the results of a critical literature review and interviews with multidisciplinary experts reflecting on the current state of integration around IAMs and opportunities to better capture social aspects within large-scale modelling processes. Our findings confirm the need to incorporate social aspects in IAMs, but highlight that how this happens in practice may depend on context, project objectives, or pragmatic choices rather than conceptual notions about what ‘good’ integration is. Nevertheless, some integration strategies are better than others, and concerns about data limitations and low capacity of the IAM community for engaging in integration can be overcome with sufficient support and complementary efforts from the broader research community. Social Media Summary Integrating social aspects in large-scale models requires complementary efforts from the broader research community.

Ecosystem restoration is inherently a complex activity with inevitable tradeoffs in environmental and societal outcomes. These tradeoffs can potentially be large when policies and practices are focused on single outcomes versus joint achievement of multiple outcomes. Few studies have assessed the tradeoffs in Nature’s Contributions to People (NCP) and the distributional equity of NCP from forest restoration strategies. Here, we optimized a defined forest restoration area across India with systematic conservation planning to assess the tradeoffs between three NCP: i) climate change mitigation NCP, ii) biodiversity value NCP (habitat created for forest-dependent mammals), and iii) societal NCP (human direct use of restored forests for livelihoods, housing construction material, and energy). We show that restoration plans aimed at a single-NCP tend not to deliver other NCP outcomes efficiently. In contrast, integrated spatial forest restoration plans aimed at achievement of multiple outcomes deliver on average 83.3% (43.2 to 100%) of climate change mitigation NCP, 89.9% (63.8 to 100%) of biodiversity value NCP, and 93.9% (64.5 to 100%) of societal NCP delivered by single-objective plans. Integrated plans deliver NCP more evenly across the restoration area when compared to other plans that identify certain regions such as the Western Ghats and north-eastern India. Last, 38 to 41% of the people impacted by integrated spatial plans belong to socioeconomically disadvantaged groups, greater than their overall representation in India’s population. Moving ahead, effective policy design and evaluation integrating ecosystem protection and restoration strategies can benefit from the blueprint we provide in this study for India.

Energy system optimisation models (ESOMs) are widely used for policy analyses particularly on topics related to climate change mitigation and renewable energy transition. Using ESOM to investigate regions that potentially require significant expansion of grid infrastructure requires incorporation of grid expansion problem within the optimisation. This study presents the development of SELARU, a Mixed-Integer Linear Programming (MILP) model for spatially explicit long-term energy infrastructure planning. The model is used to investigate the case study of Indonesia using various spatial treatments to demonstrate the impact of detailed spatial depiction of grid expansion. Results reveal significant difference in renewable energy deployment trajectory (up to 315% increase in generation capacity) between high-resolution spatial depiction of grid expansion vis-à-vis non spatially explicit energy system optimisation. SELARU’s high-resolution energy system optimization modelling also provides detailed information on the geographical extent of grid expansion requirement, which provides more realistic insights on governance challenges of renewable energy transition. Careful consideration of spatial representation is crucial when ESOM is used to evaluate scenarios that concern technology selection such as renewable energy deployment or climate change mitigation.

Biophysical productivity of production systems involving oil palm, rubber, cacao, coffee and coconut on mineral soil using the WaNuLCAS model developed by ICRAF. The WaNuLCAS model (van Noordwijk and Lusiana 1999; van Noordwijk et al., 2011) is a generic tree-crop growth model for a wide range of agroforestry systems that considers both aboveground (light) and belowground (soil, water, and nutrients). Yield and carbon sequestration calculation of the corresponding production systems in peatland were developed using a hybrid approach through adjusting results for mineral soil production system with productivity gaps identified through literature review. Finally, land suitability and statistical data analyses were conducted to estimate productivity of sago and pineapple. The dataset cover current practices and potential management improvements of tree crops cultivation that can increase the benefits to people’s livelihood while contributing to climate change mitigation and biodiversity. Results from this study, in combination with other tree-growth and forest regrowth related information, will provide a comprehensive overview on ecological and economic impact of restoration interventions ranging from improved management to ecological restoration. This dataset was created as a part of the RESTORE+ project. The details are described in a separate document (see https://doi.org/10.5281/zenodo.7937135).

Carbon dioxide removal (CDR) figures prominently in modelled pathways to achieve the Paris Agreement's goal of limiting global warming to 1.5-2°C compared to pre-industrial levels. However, national roles and responsibilities to deliver CDR have been informed with CDR quota analyses that focus on developed economies and global major emitters. This study extends the discussion to implications for developing countries. For that purpose, we employ a diverse set of allocation methods on a wide range of global emissions scenarios to address equitability and uncertainty in sharing the burden of climate change mitigation. We further focus on tropical developing countries due to their large potential for natural climate solutions (NCS) that deliver CDR. Our analysis indicates the potential for stringent CDR quotas for the top seven countries that contribute ∼60% of pantropical cost-effective NCS potential, with median national quotas across emissions scenarios ranging from 0.1-29 GtCO2. However, the results reveal strong heterogeneity of quotas and inherent bias across allocation methods making agreement on an 'equitable' quota unlikely. Competition among NCS and non-NCS CDR options may arise when ambitious CDR quotas are implemented in countries with vast forest areas or large potential for expansion of tree cover. Therefore, it is important to not use CDR quotas to evaluate national climate actions or to inform climate targets that could exacerbate trade-offs between emissions reduction, biodiversity and ecosystem services in these NCS-rich countries. Instead, results from burden-sharing exercises could foster higher ambition if used to inform voluntary cooperation mechanisms. Discrepancy between perceived fairness and CDR quotas should be critically and transparently embraced to encourage acknowledgment of socio-ecological co-benefits as compensation. Such an approach will allow tropical developing economies to prioritise protection and restoration of nature in their climate change mitigation pathways.

The data on spatial simulation units (SimU) for Indonesia, version 0.1 (IND_SimU_v01) created during the RESTORE+ project. The primary purpose of the dataset is to provide spatially allocated inputs for bio-physical models (EPIC-IIASA, G4M, and WaNuLCAS), and economic optimization models (GLOBIOM) with minimum data content necessary for running the models, including the data on terrain, soil, and administrative units attributed to the spatial simulation units (SimU). The IND SimU v.0.1 data product consists of i) the spatial reference data (grid and lattice) organized in two separate GIS layers (point and polygon features) covering the whole of Indonesia, and ii) the table data organized in several separate (structured) text files corresponding to the spatial reference data which further describe the spatial elements (polygon/point feature attribute data). The spatial reference data consist of regular spatial grid and geographically corresponding centroid point lattice at spatial resolution of 5.0 (about 9.25 km on equator) with the bounding box (decimal degrees) of S 11.0833, E 95.0000 (lower left), and N 6.0833, E 141.0833 (upper right) corners. Table data further describe spatial elements (polygon/point features) and provide users/models with a data on real areas, land surface relative areas, administrative borders, and the terrain and soil conditions within the grid cell borders (or at the centroid point). The records in the data tables either directly correspond (one-to-one) to the SimU (individual grid cells or grid cell centroids – attribute tables) or provide an extended description of the classes used in the attribute tables (attribute domain tables). The list and description of the LMS RefGrids v.0.1 spatial reference data layers and the table data is stored with the data in separate data descriptor text files. The details of the dataset creation procedures are described in the separate document (https://doi.org/10.5281/zenodo.7040984).

Here we present a geographically diverse, temporally consistent, and nationally relevant land cover (LC) reference dataset collected by visual interpretation of very high spatial resolution imagery, in a national-scale crowdsourcing campaign (targeting seven generic LC classes) and a series of expert workshops (targeting seventeen detailed LC classes) in Indonesia. The interpreters were citizen scientists (crowd/non-experts) and local LC visual interpretation experts from different regions in the country. We provide the raw LC reference dataset, as well as a quality-filtered dataset, along with the quality assessment indicators. We envisage that the dataset will be relevant for: (1) the LC mapping community (researchers and practitioners), i.e., as reference data for training machine learning algorithms and map accuracy assessment (with appropriate quality-filters applied), and (2) the citizen science community, i.e., as a sizable empirical dataset to investigate the potential and limitations of contributions from the crowd/non-experts, demonstrated for LC mapping in Indonesia for the first time to our knowledge, within the context of complementing traditional data collection by expert interpreters.

Due to the capital cost of co-firing being lower than other biomass technologies, the transformation of coal plants into co-firing facilities can potentially minimize the bioenergy cost needed to meet energy decarbonization targets. This study analyzes the impact of the co-deployment of co-firing and dedicated biomass technologies in contributing to the bioenergy cost reduction for country-level energy systems using a spatio-temporal techno-economic optimization model. Malaysia is used as a case in the analysis. Different scenarios were developed to assess the robustness of the cost reduction potential under the impact of incremental CO2 reduction targets and supply chain cost parameter variations. Our results suggest that the multi-sectoral deployment of bioenergy in energy systems is key to meeting decarbonization targets at the national scale. By also considering co-firing in the biomass technological pathway, up to 27% of bioenergy cost reduction can be enabled in the baseline case. The decrease in the supply chain cost parameter values further enhances the cost reduction potential; bioenergy costs can be reduced up to threefold. The findings have shown that developing countries such as Malaysia can benefit from the use of their rich agricultural resources to cost-effectively alleviate renewable energy poverty.

Although aspects of long-term planning are commonly taken into account in current analyses of bioenergy policy scenarios, representations of the bioenergy supply chain are often spatially aggregated. Multiple questions such as where, when, and how bioenergy is deployed have thus not been sufficiently addressed within a single modeling framework. Moreover, techno-economic models that can capture the dependencies of bioenergy supply chain variables among end-use sectors still need to be explored. The present research connects these gaps by presenting the development of a spatio-temporal techno-economic optimization model for cross-sectoral bioenergy policy evaluations under high spatial resolution and long-term temporal resolution. The research recognizes not only the need for energy decarbonization, but also the importance of improving resource efficiency in the palm oil industry, in this case, Malaysia's palm oil bioenergy industry. The findings highlight the need for multi-sectoral collaboration between the energy sectors to deliver cost-optimal energy decarbonization at the national scale. This is represented by the substitution of up to 30%, 27%, and 12% of the energy demands in the power, heat, and transport sectors with bioenergy, respectively. The conflict between policy targets was also highlighted, namely, that new policies prioritizing bioenergy in the power and transport sectors reduce CO2 more effectively than policies targeting CO2 reduction alone, however, requiring up to 37% more cost in meeting the CO2 reduction commitment. The findings also outline the requirement of co-locating bioenergy production facilities with the existing facilities (e.g., agricultural mills, coal plants) and extending the existing infrastructure network to deliver the bioenergy capacities needed to meet the policy targets.

Restoration of degraded land is an important national goal to achieve Indonesia’s environmental targets. To map both land cover and land degradation, Indonesia needs timely, high quality data and the necessary tools. We have addressed this issue by running a sequence of crowdsourcing campaigns. Our aim is not only to collect the data but to also potentially present a way for citizens to contribute to larger environmental policies and strategies. Focusing on land cover identification and tree cover change, we planned and ran a set of pilot crowdsourcing campaigns in two provinces in Indonesia. We analysed the data from these pilot campaigns, and then used the insights obtained in the subsequent crowdsourcing campaign on land cover identification, upscaled to national level, which is currently ongoing. The campaigns were run using a mobile application developed as part of the RESTORE+ project. Through this application, we presented volunteers with simple microtasks by showing them satellite images and asking a simple yes/no question as to whether the image shows a particular land cover class. The application implemented a scoring system, which additionally performs a quality control of the data contributed by the crowd, and users competed with each other to classify the satellite images displayed by the application. 692 volunteers have actively engaged in the pilot crowdsourcing campaigns and have contributed more than 2.5 million satellite image interpretations. Based on the insights from the pilot campaigns, as well as an expert consultation session in Indonesia, the crowdsourcing application was modified to ensure, first, a uniform number of interpretations across the images, and secondly, higher quality data by allowing users to focus on geographical areas familiar to them, as well as to see the larger area surrounding the target sample. We analyzed the data collected and will present issues regarding data quality, comparing the accuracy of the contributions from the volunteers with the accuracy of the data collected by a set of experts. We show that a citizen science approach is promising and can complement scientific analyses and can provide potential inputs to policies on landscape restoration. A crowdsourcing approach to image interpretation can also help to shorten the time needed for data collection, making the process more cost-effective. In addition, the collective ownership of the results ensures their legitimacy and increases the chances of data acceptance. We also focus on transparency and the importance of open data. We present how we have made data generated by the crowd accessible in order to empower citizens in exploring and process the data further, thereby actively participating in environmental decision making.

In a modeling study, optimizing the transformation of the US coal sector to achieve emissions reductions consistent with the 2°C target, we include all current coal-fired power plants of the US fleet, a large part of which will need to be replaced due to their high age. Coal-fired power plants can either be (1) replaced by higher efficiency coal plants or (2) natural gas plants while units are not yet at the end of their lifetime and can be (3) retrofitted with carbon capture and storage (CCS) or (4) retrofitted to cofire coal and biomass coupled with CCS (BECCS) thereby achieving negative emissions. Our results show that if the 2°C emissions mitigation target should be achieved, the cost-optimal way of doing so is through an early implementation of BECCS. This strategy also helps to address the US Administrations' concern for coal workers: there is a more gradual phaseout of coal, which allows to retain 40,000 jobs that would be loss due to the fleet retirement for aging. In addition, 22,000 new workers would be permanently employed in the coal sector by the end of midcentury, especially in areas where the deployments of BECCS would start already by 2030. Our modeling results indicate the Great Lakes area and the southeast United States as the greatest winners of this negative emissions strategy. If planned in an integrated and forward-looking way, climate change mitigation can boost employment and competitiveness.

We improve the accuracy of modeling burned areas using the FLAM model by identifying the hidden relationships between human and natural impacts on wildfire suppression efficiency using the deep learning-based methods. The wildfire climate impacts and adaptation model (FLAM) is able to capture impacts of climate, population, and fuel availability on burned areas. FLAM uses a process-based fire parameterization algorithm with a daily time step. The model uses daily temperature, precipitation, relative humidity and wind speed to assess climate impacts on ignition probability and fire spread. The key features implemented in FLAM include fuel moisture computation based on the Fine Fuel Moisture Code (FFMC) of the Canadian Forest Fire Weather Index (FWI), and a procedure to calibrate spatial fire suppression efficiency. The coupled FLAM and deep learning approach consists in the following steps. First, using FLAM we calibrate the suppression efficiency map by comparing model output with observed burned area (satellite data). Secondly, we use deep learning methods to identify and assess the drivers behind the calibrated map. The features used in the analysis include several socio-economic factors, including accessibility, GPP, land use maps, as well as burned areas and other parameters modeled by FLAM. Our approach allows classifying those features by their importance and find correlations between them. Finally, we implement the output of deep learning network to estimate the spatial suppression efficiency within FLAM (instead of calibrating it), and validate the approach using observed burned area. The proposed approach is implemented using the Google Earth Engine platform that provides flexibility in terms of input data sets and visualization tools. We will present the case study for Indonesia at 0.083 arc degree spatial resolution. It is planned to consider climate change impacts in more detail. Modeling burned areas and suppression efficiency can help the implementation of fire prevention policies for decision maker and provide important information for building adequate and cost-efficient fire response infrastructure.

Monitoring large forest areas is presently feasible with satellite remote sensing as opposed to time-consuming and expensive ground surveys as alternative. This study evaluated, for the first time, the potential of using freely available medium resolution (30 m) Landsat time series data for deforestation monitoring in tropical rainforests of Kalimantan, Indonesia, at sub-annual time scales. A simple, generic, data-driven algorithm for deforestation detection based on a consecutive anomalies criterion was proposed. An accuracy assessment in the spatial and the temporal domain was carried out using high-confidence reference sample pixels interpreted with the aid of multi-temporal very high spatial resolution image series. Results showed a promising spatial accuracy, when three consecutive anomalies were required to confirm a deforestation event. Recommendations in tuning the algorithm for different operational use cases were provided within the context of satisfying REDD+ requirements, depending on whether spatial accuracy or temporal accuracy need to be optimized.