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AbstractChina’s “economic juggernaut” is often noted to have arisen from successful market reforms carried out in the context of high state capacity. In contrast, we demonstrate that crucial reforms to replace central planning with markets have stalled as a result of major barriers of two types: institutional and ideational. Focusing on the electricity sector, we find that market reforms pushed by China’s central government are hindered by deep inefficiencies that arise from the legacy plan and “plan-derived” institutions of subnational governments and grid companies, against which the central state has been largely ineffective. We also uncover fascinating ideational differences of the nature and purpose of “markets” that show how they often are envisioned more as a way to extend the planner’s “toolbox,” or to offer “salvation” for ailing incumbent firms, rather than to induce efficiency. Our empirical focus on three prominent types of “market-oriented” experiments in the electric power sector demonstrate clear limits to state capacity, limits that emanate from state actors rather than merely industry, despite high-priority central government goals of increasing efficiency, integrating renewable energy, and reducing emissions from the electricity sector.

Sustainability outcomes are influenced by the laws and configurations of natural and engineered systems as well as activities in socio-economic systems. An important subset of human activity is the creation and implementation of institutions, formal and informal rules shaping a wide range of human behavior. Understanding these rules and codifying them in computational models can provide important missing insights into why systems function the way they do (static) as well as the pace and structure of transitions required to improve sustainability (dynamic). Here, we conduct a comparative synthesis of three modeling approaches--integrated assessment modeling, engineering-economic optimization, and agent-based modeling--with underexplored potential to represent institutions. We first perform modeling experiments on climate mitigation systems that represent specific aspects of heterogeneous institutions, including formal policies and institutional coordination, and informal attitudes and norms. We find measurable but uneven aggregate impacts, while more politically meaningful distributional impacts are large across various actors. Our results show that omitting institutions can influence the costs of climate mitigation and miss opportunities to leverage institutional forces to speed up emissions reduction. These experiments allow us to explore the capacity of each modeling approach to represent insitutions and to lay out a vision for the next frontier of endogenizing institutional change in sustainability science models. To bridge the gap between modeling, theories, and empirical evidence on social institutions, this research agenda calls for joint efforts between sustainability modelers who wish to explore and incorporate institutional detail, and social scientists studying the socio-political and economic foundations for sustainability transitions. [ABSTRACT FROM AUTHOR]