The development of Indonesia's energy system is characterized by slow deployment of solar and wind energy, ongoing coal power expansion and plans to further increase fossil capacities. We review the political economy factors that hinder a clean energy transition in Indonesia. We show that the actors who are most positive to decarbonization have limited influence on Indonesia's energy policies, whereas for the actors with the greatest influence, other policy objectives are more important. Even though renewable energy sources can support key policy objectives such as economic development and energy security, fossil energy better serves the demand of special interest groups. We argue that an energy transition in Indonesia will hence only be politically feasible if it boosts the profits or political power of influential actors or if a sufficient number of Indonesian actors can be convinced that renewables perform significantly better in terms of economic development and energy security than fossil fuels. We identify a range of actions to advance renewable power that might be feasible under these political constraints.
Scenarios serve as a critical tool in climate change analysis, enabling the exploration of future evolution of the climate system, climate impacts, and the human system (including mitigation and adaptation actions). This paper describes the scenario framework for ScenarioMIP as part of CMIP7. The design process has involved various rounds of interaction with the research community and user groups at large. The proposal covers a set of scenarios exploring high levels of climate change (to explore high-end climate risks), medium levels of climate change (anchored to current policy), and low levels of climate change (aligned with current international agreements). These scenarios follow very different trajectories in terms of emissions, with some likely to experience peaks and subsequent declines in greenhouse gas concentrations in this century. An important innovation is that most scenarios are intended to be run, if possible, in emission-driven mode, providing a better representation of the Earth system uncertainty space. The proposal also includes plans for long-term extensions (up to 2500 AD) to study long-term impacts, climate change-related processes on long timescales, and (ir)reversibility. This proposal forms the basis for further implementation of the framework in terms of the derivation of emissions and land use pathways for use by Earth system models and additional variants for adaptation and mitigation studies.
The global food system provides nourishment to most of the world's eight billion people, generates trillions of dollars of goods and services, and employs more than one billion people. On the other hand, it generates substantial dietary health costs and environmental harms. Policymakers are asking about the overall contribution of the global food system to social welfare and how much larger it might be on a sustainable path. This paper describes our efforts to answer these questions. We couple multiple domain-specific models into a large-scale integrated assessment modelling framework capable of quantifying the outcomes of different food-system scenarios for incomes, health and the environment up to 2050, at a highly disaggregated level. We take these multidimensional outcomes and value them using a system of nested utility functions, building on recent work in environmental economics. We find that, relative to current trends, the bundle of measures in a Food System Transformation scenario would provide a large boost to global social welfare equivalent to increasing global GDP by about 7 %. Changes in income, environment and health all contribute positively. Measures to change diets are particularly beneficial, although a caveat is that our welfare estimates exclude possible consumer disutility from dietary changes. The results are robust to changes in key utility/damage parameters.
Incentivizing and financing carbon dioxide removal (CDR) is a challenge for regulators. We show how introducing carbon debt-the obligation to remove carbon in the future-in an emissions trading scheme (ETS) can induce CDR and enable net-negative emission flows. For "clean-up certificates" that bundle emission permits with carbon debt, we characterize demand and pricing in an analytically tractable model. To ensure repayment of carbon debt, we derive the necessary value of collateral and discuss institutions as a lender of last resort. We find that introducing cleanup certificates does not reduce near-term carbon prices and mitigation efforts when they replace emission permits in the ETS, and that, by controlling the extent of carbon debt, clean-up certificates are more efficient than an ETS with full borrowing flexibility. In an exemplary calibration to a comprehensive EU ETS, we identify welfare-improving reforms that increase environmental ambition while simultaneously reducing compliance costs. With sufficiently rapid technological progress, the EU's remaining cumulative carbon budget could be halved compared to the current budget or even become negative.
Networks with complex topologies describe numerous natural and social systems. Recent studies on path multiplicity have shown strong heterogeneity in shortest paths between node pairs in real-world networks. However, the mechanism underlying this phenomenon remains unexplored. Here, we reveal that community structure is a key factor shaping path multiplicity. To explore the intrinsic factors that influence path multiplicity, we first introduce the concept of relative path multiplicity and find that community structure is more strongly correlated with path multiplicity than other network metrics. Through targeted edge-rewiring experiments, we verify the link between path multiplicity and community structure. The underlying mechanism can be interpreted as an interface-driven effect that sharply increases the number of shortest paths. Inspired by these findings, we propose a tribal-structure-based network model that reproduces phenomena observed in real-world networks. Our work enhances the understanding of network organization, with potential applications in network design and optimization.
