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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.

This document describes the methodology used to simulate spatiotemporal water regime data (i.e., water table depth and soil moisture – see dataset at https://doi.org/10.5281/zenodo.7355372) for a range of rewetting scenarios and land use classes in Indonesian peatlands. The large-scale modelling methodology is based on the Environmental Policy Integrated Climate (EPIC) model and gridded soil and meteorological information available from Indonesia. This document also describes findings from a regional assessment addressing rice productivity in potentially restored Indonesian peatlands carried out as a part of the RESTORE+ project. The impact assessment explores rice yields following assumptions on (a) nutrient management, namely the good agricultural practice and management that was back-calibrated against statistically reported regional yields, (b) separate rice growing seasons, and (c) different rewetting intensity scenarios. The analysis was carried out by the EPIC-IIASA modelling framework, addressing all peatland resources suitable for rice production, including mineral soils and shallow peat soils with a depth of less than 1 m., Funding acknowledgment: This work was supported by the RESTORE+ project (www.restoreplus.org), which is part of the International Climate Initiative (IKI), supported by the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) based on a decision adopted by the German Bundestag.

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.

The document describes version 0.1 of the Spatial simulation infrastructure for the bio-physical modelling in Indonesia (https://doi.org/10.5281/zenodo.6997967) and the procedures of its creation. Primary purpose of the dataset was to provide a consistent interface for effective integrating of the data coming from variety of resources, as well as to provide the RESTORE+ project bio-physical and economic optimization modelling suite (EPIC-IIASA, G4M, WaNuLCAS, and GLOBIOM) with necessary inputs on terrain, soil, and sub-national administrative units., Funding acknowledgement: This work was supported by the RESTORE+ project (www.restoreplus.org), which is part of the International Climate Initiative (IKI), supported by the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) based on a decision adopted by the German Bundestag.

The document describes version 0.1 of the Harmonized peatland dataset for bio-physical modelling in Indonesia (https://doi.org/10.5281/zenodo.6998051) and the procedures of its creation. Primary purpose of the dataset was to provide bio-physical models (e.g. EPIC-IIASA and WaNuLCAS) with consistent input data on peat and mineral soil distribution in Indonesia and with the peat parameters necessary for proper representation of peat hydrology in simulations., Funding acknowledgement: This work was supported by the RESTORE+ project (www.restoreplus.org), which is part of the International Climate Initiative (IKI), supported by the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) based on a decision adopted by the German Bundestag.

Summary Concerns have been voiced that implementing climate change mitigation measures could come at the cost of employment, especially in the context of the US coal sector. However, repurposing US coal plants presents an opportunity to address emission mitigation and job creation, if the right technology change is adopted. In this study, the transformation of the US coal sector until 2050 is modeled to achieve ambitious climate targets. Results show that the cost-optimal strategy for meeting 2050 emission reductions consistent with 2°C stabilization pathways is through the early deployment of BECCS and by replacing 50% of aging coal plants with natural gas plants. This strategy addresses the concerns surrounding employment for coal workers by retaining 40,000 jobs, and creating 22,000 additional jobs by mid-century. Climate change mitigation does not have to come at the cost of employment, and policymakers could seek to take advantage of the social co-benefits of mitigation.

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.

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.

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.

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.

This paper is a contribution to the ongoing debate on carbon-negative energy solutions. It deals with biomass conversion in dedicated biopower plants equipped with CCS (BECCS), or co-fired plants retrofitted with CCS in order to generate negative CO2-emissions. In this context, bioenergy refers to the use of biomass to generate electricity (i.e. biopower) in compliance with the needs of nations and regions without seasonal space heating demand. In this paper, direct-fired and co-fired systems will be addressed, combined mainly with post-combustion flue gas cleaning. The question is which CCS alternative should be preferred in order to obtain negative emissions: either building multiple smaller biopower units, or employing co-firing of biomass and coal in existing large coal power plants. Based on efficacy and the potential for mitigating greenhouse gas emissions as key indicators, some major differences between the alternatives are shown. In the event that a coal power plant equipped with CCS is readily available, more net electric energy (in MWh) can be provided from the feedstock of biomass than would be obtainable from an independent BECCS plant, although the amount of CO2 captured and stored from the biomass (per tonne) will be essentially the same. Further case-specific cost-benefit analyses will be required to determine the feasibility of carbon-negative energy solutions. Although the study is carried out from the perspective of actual biomass sources as regards biomass composition and available technology (i.e. expected efficiency levels) using Indonesian agricultural residues, its main conclusion is fairly general.

Large-scale wildfires affect millions of hectares of land in Indonesia annually and produce severe smoke haze pollution and carbon emissions, with negative impacts on climate change, health, the economy and biodiversity. In this study, we apply a mechanistic fire model to estimate burned area in Indonesia for the first time. We use the Wildfire Climate Impacts and Adaptation Model (FLAM) that operates with a daily time step on the grid cell of 0.25 arc degrees, the same spatio-temporal resolution as in the Global Fire Emissions Database v4 (GFED). GFED data accumulated from 2000–2009 are used for calibrating spatially-explicit suppression efficiency in FLAM. Very low suppression levels are found in peatland of Kalimantan and Sumatra, where individual fires can burn for very long periods of time despite extensive rains and fire-fighting attempts. For 2010–2016, we validate FLAM estimated burned area temporally and spatially using annual GFED observations. From the validation for burned areas aggregated over Indonesia, we obtain Pearson’s correlation coefficient separately for wildfires and peat fires, which equals 0.988 in both cases. Spatial correlation analysis shows that in areas where around 70% is burned, the correlation coefficients are above 0.6, and in those where 30% is burned, above 0.9.

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.

The integration of solar and wind technologies into the energy supply system is becoming increasingly attractive as their costs per unit capacity drop and tight policies are being implemented against greenhouse gas emissions. However, their contribution to a power system is uncertain due to the intermittent nature of wind and insolation. Here, we investigate a power dispatch system that aims to supply the power demand of the EU based on low-carbon generation units enabled with expansion of biomass, solar, and wind energy. A spatially explicit techno-economic optimization tool simulates the EU power sector to explore policies for renewable energy deployment. Results show utility-scale deployment of intermittent electricity is feasible when incentivized with carbon tax. Expansion of transmissions capacity helps even-out intermittency of solar and wind technologies. Spatial assessment of bio-electricity potential (based on forest and crops residues) has small contribution to the optimal generation mix, limiting its effect as baseload.

This brief examines two contrasting policy options: the implementation of zero deforestation commitments by the private sector and a complete moratorium on the expansion of large-scale oil palm plantations, and compares them to a situation without policy action. The zero deforestation commitments and the moratorium on large-scale oil palm plantations expansion could reduce cumulative deforestation by 25% and 28%, respectively, compared to a situation without policy action. They could also cut greenhouse gas emissions from land use and land-use change by 13% and 16%, respectively, over the period 2010-2030. Even under the zero-deforestation and moratorium scenarios, Indonesia is projected to increase palm oil production between 124%-97% over 2010-2030, which is partly due to higher production originating from smallholders. Both measures – the zero deforestation commitments and a moratorium of future large-scale oil palm plantations expansion – would be especially beneficial to limit future deforestation in Indonesia in a context in which global demand for palm oil is expected to keep increasing. Foresight tools can equip stakeholders and policy makers with data and information to allow for evidence-based policy making. This will permit planning for reducing deforestation and greenhouse gas emissions, and finding options acceptable to all stakeholders involved.

From 1980 to 2002, China experienced a 5% average annual reduction in energy consumption per unit of gross domestic product (GDP). With a dramatic reversal of this historic relationship, energy intensity increased 5% per year during 2002–2005. China's 11th Five Year Plan (FYP) set a target of reducing energy intensity by 20% by 2010. This paper assesses selected policies and programs that China has instituted to fulfill the national goal, finding that China made substantial progress and many of the energy-efficiency programs appear to be on track to meet – or in some cases exceed – their energy-saving targets. Most of the Ten Key Projects, the Top-1000 Program, and the Small Plant Closure Program will meet or surpass the 11th FYP savings goals. China's appliance standards and labeling program has become very robust. China has greatly enhanced its enforcement of new building energy standards but energy-efficiency programs for buildings retrofits, as well as the goal of adjusting China's economic structure, are failing. It is important to maintain and strengthen the existing energy-saving policies and programs that are successful while revising programs or adding new policy mechanisms to improve the programs that are not on track to achieve the stated goals.

Policy Options for Encouraging Energy Efficiency Best Practices in Shandong Province’s Cement Industry Exploring design options for a sectoral approach Lynn Price, Nan Zhou, Hongyou Lu China Energy Group Environmental Energy Technologies Division Lawrence Berkeley National Laboratory Emiel van Sambeek, Ping Yowargana, Liu Shuang Azure International Jiang Kejun Energy Research Institute Reprint version of the report submitted to Strategic Programme Fund, UK FCO in July 13, 2012 July 2012 This work was funded by Strategic Programme Fund, UK FCO through the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

E RNEST O RLANDO L AWRENCE B ERKELEY N ATIONAL L ABORATORY Assessment of China’s Energy-Saving and Emission-Reduction Accomplishments and Opportunities During the 11 th Five Year Plan Mark D. Levine, Lynn Price, Nan Zhou, David Fridley, Nathaniel Aden, Hongyou Lu, Michael McNeil, Nina Zheng, Qin Yining China Energy Group, Energy Analysis Department Environmental Energy Technologies Division Lawrence Berkeley National Laboratory Ping Yowargana Azure International - Beijing April 2010 This work was supported by the China Sustainable Energy Program of the Energy Foundation through the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.The U.S. Government retains, and the publisher, by accepting the article for publication, acknowledges, that the U.S. Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. Government purposes.