Over the past two decades, states and cities implemented low-carbon energy development, renewable portfolio standards, demand-side management (DSM), renewable energy production incentives, green building requirements, regional carbon trading agreements, and other energy-based economic development initiatives. Yet the dearth of state-level and substate-level models makes it difficult to predict the effects of such actions. This article addresses this shortcoming by presenting the performance results of the new Indiana Scalable Economy and Energy Model (IN-SEEM)—a model utilizing a dynamic, simultaneous equations framework—and demonstrates the model’s capabilities with an analysis of electricity price increases from a DSM program in the state of Indiana. Overall performance of the model is strong, with high adjusted R² values and low mean absolute percent errors for most of 30 endogenous variables. A DSM price increase analysis finds variation in impact across the state’s 10 major economic sectors and small changes in energy consumption.

在过去的二十年中，各州和城市实施了低碳能源开发，可再生能源投资组合标准，需求方管理（DSM），可再生能源生产激励措施，绿色建筑要求，区域碳交易协议以及其他基于能源的经济发展计划。但是，由于缺乏州级和子州级模型，因此很难预测此类行为的效果。本文通过介绍新的印第安纳州可扩展经济和能源模型（IN-SEEM）的性能结果（使用动态联立方程框架的模型）来解决此缺点，并通过对DSM的电价上涨进行分析来展示该模型的功能在印第安纳州的计划。该型号的整体性能强劲，调整后的R高对于30个内生变量中的大多数，有^2个值和较低的平均绝对百分比误差。DSM价格上涨分析发现，该州的10个主要经济部门的影响各不相同，能源消耗的变化很小。