Songmin Yu

Translating global climate targets into national decarbonization roadmaps is profoundly uncertain. To navigate this uncertainty for China, we employ a national-scale energy system model developed in the MESSAGEix framework-calibrated to China's energy balances-that uniquely combines provincial-level resolution for key sectors with a high-granularity representation of intra-annual (48 time slices) power system dynamics. Across three temperature targets (1.5, 1.6, 2.0°C) and six allocation principles, our analysis reveals that wind and solar consistently emerge as "no-regret" pillars, CCS is essential for heavy-industry abatement, and hydrogen's sourcing shifts with budget stringency. A critical systemic co-dependency exists across scenarios with stringent emissions constraints: the power sector must transform into a net carbon sink to enable the decarbonization of heavy industry, creating stark path dependencies across technology choices. The 1.6°C pathway under the Grandfathering principle aligns with China's 2060 neutrality pledge and offers a detailed blueprint for this transition. Our provincial-level analysis distinguishes high-stakes decisions from robust "no-regret" investments, offering a framework to guide China's journey to carbon neutrality.

The building sector is pivotal for achieving climate neutrality, requiring sophisticated modeling tools to guide the energy transition. It is highly heterogeneous with variety in both the built environment and in its decision-makers. While many sectoral models exist, most of them fall short of capturing the barriers and limitations in the energy transition as they lack high spatial resolution and a detailed representation of heterogeneous building characteristics and local infrastructure constraints. To address this gap, we present RENDER-Building, a new agent-based model (ABM) designed for high-resolution analysis of building stock transformation. We validate and apply the model to the German building stock to simulate potential transformation pathways until 2050 under three distinct scenarios. The individual buildings are the agents here with detailed attributes, located in a settlement type in a NUTS3 region. The model explicitly considers the availability of energy infrastructure and simulates agents’ decisions about renovation and technology adoption based on bounded rationality. Our case study’s results indicate that even with ambitious measures, Germany’s building sector may miss its short-term emission targets due to the inertia of the existing stock. A transformation pathway considering realistic challenges could substantially exceed the short- and long-term emission targets, necessitating difficult and potentially costly interventions to get back on track. Our study demonstrates the utility of high-resolution ABMs in providing nuanced, actionable insights for policymakers, helping them to navigate the complexities of the building sector’s energy transition.

Technological advancements and behavior shifts are reshaping households’ energy consumption patterns, necessitating advanced models to quantify their behavior, energy system operation, and interactions in the energy communities. While various models address these aspects individually, there is a lack of a unified framework that covers them holistically. This paper presents FLEX, a modeling framework consisting three interconnected components that are designed to feed the output of one into the next. First is FLEX-Behavior, which simulates hourly household energy demands using a Markov core. Second is FLEX-Operation, which models hourly operation of household energy systems across three modes: simulation, perfect-forecasting optimization, and rolling-horizon optimization. Its results are validated with detailed physics-based building simulation software. Third is FLEX-Community, which models the peer-to-peer electricity trading among community members and battery operation of the aggregator. Finally, demonstration results are provided to show the capabilities of FLEX in potential applications for supporting policy design. In summary, FLEX advances existing approaches by bridging detailed household-level behavior and energy system modeling with community-scale optimization, addressing the trade-off between computational tractability and household-level accuracy in the modeling of aggregator-operated energy communities. However, limitations also lie in the requirement of high-quality micro-level data for robust estimation and validation. Future research could investigate system-level dynamics between energy communities and power systems, including participation in ancillary services markets and the evolving regulatory frameworks governing community operations.

The electrification of households’ heating systems will lead to an increase in the electricity demand, which will necessitate additional investments in the grid infrastructure. The interaction with other technologies, including PV, batteries, electric vehicles (EVs), and home energy management systems (HEMSs), further complicate the situation. In this study, we analyze the following question: How will prosumaging households, who consume, produce and manage their energy consumption with HEMS, impact the future reinforcement costs of the electricity distribution grid? We conducted case studies for two urban areas, Murcia in Spain and Leeuwarden in The Netherlands. First, by developing scenarios on the uptake of electrified heating systems, PV installations, battery storage, EVs and HEMSs, the energy demand of each building is modeled for the two areas under different scenarios. Then, the buildings’ electricity load profiles were provided to a second model, to calculate the necessary distribution grid infrastructure to cover this demand on a granular spatial level. Results show that low voltage lines and transformers will need significant investments, especially in the regions where a high share of conventional heating systems are replaced by heat pumps but also in regions where the aggregate electricity peak demand is reduced.

Agent-based models (ABMs) characterize physical, biological, and social economic systems as dynamic interactions among agents from a bottom-up perspective. The agents can be molecules, animals, or human beings. The interactions can be water molecules forming a vortex, ants searching for food, or people trading stocks in the market. Agents’ interactions can bring emergent properties to a system and turn it into a complex system. The core reason for using ABMs is usually to model such mechanisms. Besides, taking social economic systems as example, ABMs are also flexible to consider agents’ (1) heterogeneity (e.g., wealth, risk attitude, preference, decision-making rule, etc.) based on micro-data; and (2) bounded rationality and adaptation behavior based on psychological and behavioral studies. Melodie is a general framework for developing agent-based models (ABMs) in Python. It is published and maintained on the GitHub organization page of ABM4ALL, a developing community among agent-based modelers for sharing ideas and resources. Together with the code repository, we have also published the documentation of Melodie, including a tutorial explaining how a minimum example - an agent-based covid contagion model - can be developed with Melodie step by step.

This article proposes an agent-based policy simulation framework that can be applied to the cases satisfying: 1) the agents try to maximize some intertemporal preference and 2) the impacts of different factors on agents’ behavioral tendency are monotonic. By combining the simulation and optimization methods, this framework balances the flexibility and validity of agent-based models (ABMs): the sigmoid function is modified and used to model agents’ decision-making rules, and the evolutionary training method is used to calibrate agents’ behavioral parameters. Based on an example for the emission trading scheme, the application of the framework is presented and evaluated in detail.

Though sharing a similar practice form, the emission trading scheme is distinguished from traditional financial markets: firms coordinate three abatement options at the micro level, including allowance trading, output adjustment, and low-carbon technology adoption. Then, at the macro level, this leads to dynamic interactions among allowance market, output market, and low-carbon technology diffusion. This is the fundamental characteristic of the emission trading scheme, and modeling the dynamics behind is a major difficulty for relevant studies, especially when following complexities are considered: (1) different planning horizons of the three abatement options, (2) heterogeneity among sectors and firms, and (3) details of firms’ production and optional low-carbon technologies. Aiming at this difficulty, we establish an agent-based model for the emission trading scheme, and within a novel multi-level time frame, the fundamental characteristic is reflected and the complexities are considered. Firms’ production and low-carbon technologies are discretely modeled at a process level from a bottom-up perspective, and based on European data, our model is calibrated to cover 5 industrial sectors, 11 emission-intensive products, 25 production processes, and 52 low-carbon technologies. With this model, the emergence properties and uncertainty of the system are captured, and the non-linear impact of the abatement target is reflected and discussed. We find that, after a certain level, higher target leads to lower allowance price uncertainty but stronger output impact, which is a trade-off for setting the abatement target.