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«NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy, operated by the Alliance for ...»

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2. The indirect effect refers to the increase in economic activity that occurs when a contractor or vendor receives payment for goods or services delivered and he or she is able to pay others who support the business. This includes the equipment manufacturer or wholesaler who provides the products (solar panels, insulation, heating system, windows, etc.). It also includes the bank that provides financing to the contractor, the vendor’s accountant, and the owner of the building where the contractor maintains its local offices, and so on.

3. The induced effect results from the spending of worker earnings associated with direct and indirect spending related to energy efficiency expenditures. This includes spending on food, clothing, housing, transportation, recreation, and other goods and services that workers typically purchase with their paychecks.

Moreover, the installation of energy efficiency measures usually reduces electricity and/or natural gas use in a home and enables the household to meet power, heating, cooling, and lighting needs at a lower total cost. This lower cost of home operation makes more money available for individuals and families to spend or invest in the local economy.

2.2 Analyzing the Spending from the CSLP To analyze the spending on CSLP energy efficiency upgrades (including renewable energy technologies), actual expenditures are matched with appropriate Boulder County- and Coloradospecific industry multipliers. 5 The multipliers reflect the direct, indirect, and induced impacts supported by a $1 million expenditure (change in final demand) for goods or services purchased from a given industry sector.

This analysis includes all changes in consumer and business spending that occur during the actual construction or installation for program measures as well as the ongoing spending of resulting energy bill savings. The impacts from the construction or installation are relatively short-term.

That is, the impacts are limited primarily to the period of time during which the actual upgrades and spending occur. In this analysis, the initial construction-related impacts occur over approximately a one-year period from June-July 2009 through June-July 2010. The spending of energy bill savings and resulting reduction in utility revenues happens each year for the life of the measures, typically 20 to 30 years.

Much of the short-term job creation from energy efficiency programs is derived from payments made to in-county contractors and businesses, versus out-of-county contractors and businesses.

When in-county contractors or businesses receive money for goods and services, more of the money stays in the local economy. Local contractors usually hire more local residents to work for them, and they typically spend more money in the local area on goods and services (indirect effects). Out-of-county spending—paying contractors or purchasing goods or services from businesses outside the county—is commonly referred to as monetary leakage. A monetary leakage provides little benefit to the local area. One exception might be when local residents are employed by the out-of-county businesses or when some of their products are locally manufactured.

Ongoing job creation is derived in large part from the difference between jobs within the utility and fuel supply sectors and jobs that are supported by the spending of energy bill savings in other sectors of the economy. For example, when residents pay their utility bills, most of the money leaves the local area to purchase fuels, maintain power plants, and support utility operations in general. On the other hand, when residents have savings from lower utility bills, they are able to spend some of those savings in the local area by purchasing goods and services and supporting a variety of local businesses.

This analysis is based on a detailed assessment of CSLP-related customer spending, using data available for 598 residential energy retrofit projects. It includes not only those dollars loaned to Boulder County residents through property tax bond financing but also additional spending by program participants, as documented on the invoices. Table 2.1 shows the actual financing directly for measure expenditures (i.e., not related to loan fees, reserve accounts, or other costs) totaling just over $9 million. These expenditures account for 71% of the $12.7 million in total spending related to these measures. To the extent that information on energy-related rebates from the state and utility companies was documented, it is included in the analysis. Similarly, where In this study we have adapted industry multipliers derived from the 2008 IMPLAN model for the analysis.

See Minnesota IMPLAN Group, Hudson, WI, www.implan.com.

information was available on participant spending that was alternatively financed (for example, project add-ons paid for with cash), it was also included in the analysis.

Additional residential projects were completed under the CSLP program (for a final loan total of about $9.8 million), but documentation was not available in time to be included for this analysis.

Table 2.1.

Climate Smart Loan Program 2009-2010 Residential Summary Data Just over $10 million (79%) of the documented efficiency and renewable energy investments (i.e., payments to contractors and vendors) were spent within Boulder County. 6 Typically, 85%-90% of energy efficiency and renewable energy installations are completed by local contractors and dealers. As discussed in Section 1, the profile of participating businesses for the Boulder County CSLP was much different. Only 171 (58%) of the 295 contractors studied for this analysis were located in Boulder County. The rest were from various locations throughout the Denver metro area.

Similarly, the I-O model would typically assume that all in-county contractors’ employees would live in Boulder County. However, Boulder County data reveal that at least 30% of in-county contractors’ employees live and spend most of their earnings elsewhere, possibly because the multi-county Denver area is so contiguous and offers many affordable housing options outside of Boulder County. 7 There are more local than nonlocal residents employed by local contractors, and all workers (local and nonlocal) spend money locally while working; these are mitigating conditions that would, on balance, increase local economic benefits associated with the program.

A detailed breakout of spending by measure is included in the next section of this report.

This estimate is an average, based on responses to an online survey of program contractors conducted in August

2010. Anecdotal evidence from interviews with program contractors located in Boulder County in June and July 2010 suggests that in many instances the percentage of employees living in Boulder County is significantly higher.

However, quantifying such impacts is beyond the scope of this analysis. A qualitative assessment is offered in Section 3 of this report.

For purposes of estimating current and future energy bill savings, the analysis assumes that energy prices remain at 2010 levels. This is partly due to the difficulty of accurately predicting future energy prices, but also because it is simpler to match energy prices within an I-O model based upon fixed price relationships. Many analyses would typically apply a 2%-5% annual energy 8 cost escalation rate. The utility bill savings noted in Table 2.1 reflect average savings by all participants. Due to the limited amount of information available from the utility bill analysis, no distinction has been made (nor were adjustments made) for the types of measures installed, measure cost, energy saving potential, or payback periods, or for participant homes that added square footage (or other measures)—all conditions that could result in net increased energy use.

Some participants had higher utility bills when compared with their previous bills, but most participants experienced significant reductions in energy use and utility bills. 9 An examination of possible reasons for this is included in Section 3 of this report, Qualitative Assessment.

Considering historical price increases in electricity and natural gas, the utility bill savings expressed here are conservative estimates. There is little doubt that utility prices will continue to rise and that resulting energy bill savings will increase over time.

Finally, it should be noted that the full effects of the Boulder PACE program are not accounted for, due to the conditions and impacts discussed further in Section 3. For example, there is no documentation of county residents who did not receive CSLP financing but made alternatively financed energy improvements using information they received from the CSLP program, yet there is evidence that their spending was significant. As another example, the CSLP program staff spent time and budget on program design and first-year implementation, making notes for future-year improvements. Future program benefits would likely be greater than those reported here.

2.3 Macroeconomic Impacts The economic analysis for the Boulder County CSLP was carried out by evaluating the net changes in energy expenditures brought about by the investments in energy efficiency and renewable energy (primarily solar PV). Section 1 of this report describes the types of program measures that would qualify for financing and the process for obtaining financing. Actual participant investments and utility bill savings data were used to estimate both local and statewide impacts. The change in spending generates a net impact for Boulder County and for the state as a whole.

Table 2.2 summarizes the investments for each measure during the 2009-2010 period of analysis, as well as the local contractor share and sales tax generated.

Average electric and gas utility bill savings for Xcel customers who participated in the Boulder County CSLP were provided by Tim Hillman, senior energy engineer at Symbiotic Engineering, in December 2010. Symbiotic Engineering is currently analyzing participant utility bills for Boulder County from other utilities in the county.

According to the preliminary analysis completed by Symbiotic, 20% of natural gas customers and 25% of electricity customers had increased energy consumption.

Table 2.2 ClimateSmart Loan Program 2009-2010 Residential Summary Data by Measure

As the table indicates, spending on PV systems totaled $6.8 million. This was the single largest measure in terms of dollars spent, accounting for almost 54% of total investments. Windows and doors were second, accounting for about 18%, followed by air and water heaters at about 14%.

Another four measure categories accounted for the remaining 15% of participant investments.

With this measure data, we were able to analyze the macroeconomic impacts. The first of the three impacts evaluated here is the net contribution to the employment base as measured by fulltime equivalent jobs. The second impact is the net gain in wage and salary compensation, measured in millions of 2010 dollars. The final category of impact is the net contribution to output (i.e., economic activity), also measured in millions of 2010 dollars. In other words, once the gains and losses are sorted out for each measure, the analysis provides the net benefit of the measure in terms of the overall economy.

The following table summarizes the economic impacts of the investments by measure type.

Unlike utility bill savings, which continue to provide benefits for the life of the energy efficiency measure, installation (or construction) impacts are considered one-time or short-term impacts. In other words, the installation-related impacts noted below occur when the actual work is being done and for a short time afterwards. Similarly, the impacts only account for spending that occurs in Boulder County or in the state as a whole. To the extent that equipment or products such as solar panels, roofing, or insulation are manufactured and/or purchased out of the county or state, the expenditures (or a portion of them) are treated as monetary leakages, providing no benefit to the region being analyzed.

Table 2.3. Summary of Macroeconomic Impacts for Installation by Measure

Some aspects of this table are worth noting before focusing on the overall impacts in more detail.

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