Spatial Energy Efficiency Patterns in New York and Implications for Energy Demand and the Rebound Effect

ABSTRACT

This study uses a spatial Durbin error model (SDEM) approach to analyze adoption trends for residential energy-efficiency measures (EEMs) in New York state. Model results are based on socioeconomic, building, and household demographic characteristics during the 2012–2016 period. Our study’s results confirm that a positive correlation exists between EEM uptake and multifamily buildings, gas-heated homes, education effects, and spatial spillover effects among neighboring ZIP codes. The results show that building attributes hold a relatively high explanatory power over EEM adoption compared with socioeconomic characteristics. Our results show that energy-efficiency policies can create positive and significant neighborly effects in promoting EEM adoption. The developed SDEM methodological framework provides useful insights in identifying energy-efficiency opportunities that exist in rural, suburban, and urban communities, highlighting the need to review policy incentives periodically to address underlying changes in the built environment and spatial disparities in energy-efficiency investments.

KEYWORDS:

• Energy efficiency
• spatial spillover effects
• spatial Durbin modeling
• neighborly emulation
• rebound Effects
• new York
• energy-efficiency measures

Acknowledgments

We thank three anonymous reviewers and the editor for their valuable suggestions, which improved the quality of this article substantially. Any errors that remain are the sole responsibility of the authors.

Credit authorship contribution statement

Joseph Nyangon: Conceptualization, Methodology, Software, Formal analysis, Data curation, Writing - original draft, Writing - review and editing, Visualization. John Byrne: Validation, Writing - review, and editing, Project administration.

Declaration of competing interest

The authors declare no competing interests.

Supplementary material

Supplemental data for this article can be accessed publisher’s website.

Notes

¹ These innovations include urban infrastructure development and land-use planning, EE policy and program design, improvement in market and regulatory environment, etc. For example, implementation of city-scale urban sustainability, should prioritize innovations targeting low-income households in driving EE investments, such as modal shifts that promote cost-effective EE technologies, on-site distributed electricity generation, subsidies supporting EE upgrades, improving building codes and standards, EE business model innovation, and strengthening partnership and collaboration with the building construction industry (Bertoldi and Mosconi 2020; Taminiau and Byrne 2020).

² The New York state’s SBC programs are administered by NYSERDA, and collectively fall under New York Energy $mart^SM public benefits program. The New York Energy $mart^SM programs support an accelerated market penetration of energy-efficient technologies.

³ Neighborly emulation is driven by shared energy markets, extent of connections with neighboring parameters in the built environment, norms and governance, the influence of residents’ groups, and existing local economic configurations.

Additional information

Funding

This material is based upon work supported in part by the National Science Foundation (NSF) and the U.S. Department of Energy (DOE) under NSF CA No. EEC-1041895. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect those of NSF or DOE.