We describe a newly developed detailed industry module in the Global Change Analysis Model (GCAM). GCAM-USA is an open-source human-Earth systems model that represents key interactions across economic, energy, water, and land systems with state-level energy and socio-economic detail for the United States embedded within a global model. The GCAM-USA detailed industry module is developed to provide insights on the US industrial sector responses to changing prices, policies, and technology options. State-level industrial output is simulated for 12 sub-sectors that each consume a state-specific mix of 11 energy services. Sectoral service demand calibrated with projections of industrial indicators of the Energy Information Administration’s Annual Energy Outlook out to 2050. The new industrial sector representation, which are integrated with GCAM's existing building, transportation, and energy transformation sectors, allows integrated analysis of the energy system, emissions, and mitigation at the US state level.

Decarbonizing the U.S. electric power sector will require massive deployment of clean energy infrastructure, including utility-scale solar photovoltaics (solar PV) and other renewables. This deployment, though, must comply with local zoning ordinances, which impose a nationwide patchwork of restrictions on where deployment can actually occur. While zoning restrictions on deployment may be developed for legitimate purposes to protect public health and safety, they could impede or increase the costs of decarbonization of the electric power sector, but no research in this area exists. We quantify the role of utility-scale solar zoning ordinances on power sector decarbonization across the Great Lakes region (Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin) by integrating a first-of-a-kind database of 6,300 rural community zoning ordinances into a power system planning model. Our results indicate zoning ordinances can play a pivotal role in shaping sub-regional and regional decarbonization outcomes. Relative to no ordinances, solar zoning ordinances reduce total available capacity of solar PV by 52% (or 1.6 TW) across our region. Currently, however, the biggest zoning barrier to deployment is zoning ordinances which are silent on utility-scale solar, often interpreted as a de facto ban. This absence of guidelines decreases available capacity by 31% across the region and up to 59% at the state level. Outright bans—by explicitly disallowing solar—contributes another 6% reduction across the region and up to 13% reduction at the state level. Deployment restrictions translate to up to 4 GW greater investment needs and 5.6% greater PV investment costs to achieve a 10% PV generation target. Starker shifts occur at the state level, e.g. Wisconsin sees a 40% reduction in PV investments due to zoning restrictions; these investments shift to other states with laxer ordinances, e.g. Illinois. Our results underscore the need for planning that aligns local zoning laws with state and regional decarbonization objectives.

In response to the challenges posed by rapid urbanization, this study explored relationships between urban metabolism (UM) and digital twin (DT) technologies, aiming to advance sustainable urban development. A pilot high-fidelity DT representing a university building and its surrounding streetscape was crafted by integrating CAD design drawings and Building Information Modeling (BIM) technologies using Unreal Engine. The DT incorporated both physical and analytical environments and it was further enhanced by integrating a database developed for the University of Pittsburgh annual greenhouse gas (GHG) inventory. The created model allows for high-fidelity visualization of the building's behavior during routine activities, providing a meaningful basis for comparison with UM analyses.

This innovative approach holds promise for sustainable urban design and planning, harmonizing diverse data streams through DT technologies. The potential impact of this research extends to the thorough tracking, mapping, and analysis of critical resource flows such as construction materials, water, energy, and waste, implementing circular economy strategies within the built environment. The use of UM may facilitate resource-efficient opportunities and resource recovery and reuse to minimize the environmental footprint of urban developments.

Results show how the integration of DT technologies and UM analysis streamlines data collection processes, supporting its standardization and fostering sustainable urban practices. This study represents a potential advancement to promote circularity objectives in the built environment. By adopting this framework, cities can pave the way for new production and consumption patterns that prioritize the responsible use of natural resources, contributing to a more sustainable and resilient future.

The energy infrastructure that supports cooling requirements in buildings is anticipated to undergo rapid change and growth in the coming decades. Climate goals drive ambitious electrification targets and warrant the conversion of existing energy systems. As space cooling in commercial buildings consumes 11% of the total electricity and is expected to see a growth of 38% by 2050, gaining a deeper understanding of cooling requirements in existing buildings becomes imperative to improve energy efficiency, resilience, and flexibility.

This study presents simple and scalable data-driven solutions to characterize the cooling requirements in a portfolio of 119 co-located commercial buildings in a warm-summer Mediterranean climate. Factoring out geography-driven differences provides a unique opportunity to characterize cooling needs of a heterogeneous set of buildings. Cooling loads (MJ/m2) show 7-34x variation among different classes of commercial buildings over the years, with the highest loads in medical buildings and the lowest in commercial residential buildings. We create interpretable regressions for cooling loads that can be used as data-driven benchmarks. Our models capture daily variability: they explain over 70% of variance for buildings that collectively represent 85-94% of the portfolio’s overall cooling load in five different years. Our results indicate that there is a strong usage-driven heterogeneity across buildings, both in base load intensity, which we define as the cooling use intensity at 18°C (64.4°F), and in sensitivity to the drivers that we identify. The portfolio’s base load cooling intensity is 2.6-3.0 MJ/m2 and we identify Outside Air Temperature (OAT) as the most important driver of cooling consumption. Consumption increases by 7.6-9.8% for every 1°C (~1.8°F) increase in average daily OAT. A weekend indicator variable is the next most significant feature, especially in small-sized buildings. Overall consumption reduces by 7-8% on the weekends. Other weather-related variables including solar radiation, wind, and relative humidity have a smaller influence on cooling load in the region.

The methods presented here provide building researchers and managers with a new set of analytics for developing their own data-driven building performance indicators, which can be used for targeting demand response and efficiency measures, identifying unusual consumption behavior, and opportunities for energy retrofitting. Further, these models can provide benchmarks for predicting future energy demand growth in response to changing climate.