Economic feasibility of energy efficiency measures in residential buildings

Sadineni, Suresh B.; Boehm, Robert F.

doi:10.1016/j.renene.2011.04.006

Cited by 50 publications

(13 citation statements)

References 7 publications

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“…Even if certain energy retrofit interventions appear to be profitable, particularly in the residential sector, sensitivity analysis highlights that Net Present Value (NPV) of an investment may fall below zero, due to minor changes in the input variables (Zalejska-Jonsson, Lind, & Hintze, 2012). Moreover, positive outcomes imply the willingness to accept a moderate return on investment (Copiello, 2015), sometimes only a few tens of basis points higher than threshold represented by the gross yield of government bonds (Sadineni, France, & Boehm, 2011;Zalejska-Jonsson et al, 2012). In turn, this means a low risk premium rate, hence doubts may arise whether or not it is out of the market, with respect to property investment.…”

Section: Literature Reviewmentioning

confidence: 99%

Green housing: Toward a new energy efficiency paradox?

Copiello

Bonifaci

2015

Cities

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Section: Literature Reviewmentioning

confidence: 99%

Green housing: Toward a new energy efficiency paradox?

Copiello

Bonifaci

2015

Cities

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“…Yet, while longterm savings from improvements often exceed costs to building owners, motivating homeowners to spend money upfront on energy efficiency is difficult. Many voluntary rebate programs, especially for residences, do not tie rebates with achieved energy savings (Greening et al, 2000;Morrissey and Horne, 2011;Sadineni et al, 2011). Moreover, actual energy savings from home retrofits is often less than modeling estimates, though utilities report estimates as part of assessing home retrofit program impacts (Brown, 2012;CPUC, 2016b).…”

Section: Introductionmentioning

confidence: 99%

Structural, geographic, and social factors in urban building energy use: Analysis of aggregated account-level consumption data in a megacity

et al. 2016

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Building energy use varies widely across metropolitan Los Angeles. Building age, household income, home ownership rates, and land use are all correlated with energy consumption. High-income areas use more energy per building, while lower-income areas use more energy per square-foot. Account-level energy use data can help local governments devise conservation strategies. Energy efficiency programs need evaluated using energy consumption data. a r t i c l e i n f o b s t r a c tResidential and commercial buildings comprise approximately forty percent of total energy consumption and carbon dioxide emissions in the U.S. Yet, while California spends $1.5 billion annually on energy efficiency programs, limited research has explored how building energy consumption varies within cities, including the social and structural factors that influence electricity and natural gas use. We present results from an analysis of aggregated account-level utility billing data for energy consumption across the over two million properties in Los Angeles County. Results show that consumption in L.A. County varies widely with geography, income, building characteristics, and climate. Several higher-income areas have greater total energy use per building even in cooler climates, while many lower-income regions rank higher for energy use per square-foot. Energy consumption also correlates with building age, which varies widely throughout the region. Our results demonstrate the many complex and interrelated factors that influence urban energy use. While billing data is critical for devising energy efficiency programs that actually realize estimated savings and promote more sustainable cities, opening access to such data presents significant challenges for protecting personal privacy. The presented approach is adaptable and scalable to cities seeking to develop data-driven policies to reduce building energy use.

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“…Ardente et al [3], Mahlia et al [17], Sadineni et al [23], Hens and Verbeeck [10], and Houri and Khoury [12]. However, these studies do not present a general method that is applicable to a wide range of buildings and energy efficiency measures to select the technologies among all the available ones according to their energy consumption, energy cost, and CO 2 emission to optimize a given objective function subject to budgetary constraints.…”

Section: Literature Reviewmentioning

confidence: 99%