The development of Energy Efficiency Standards and Labeling (EES&L) began in earnest in India in 2001 with the Energy Conservation Act and the establishment of the Indian Bureau of Energy Efficiency (BEE). The first main residential appliance to be targeted was refrigerators, soon to be followed by room air conditioners. Both of these appliances are of critical importance to India's residential electricity demand. About 15percent of Indian households own a refrigerator, and sales total about 4 million per year, but are growing. At the same time, the Indian refrigerator market has seen a strong trend towards larger and more consumptive frost-free units. Room air conditioners in India have traditionally been sold to commercial sector customers, but an increasing number are going to the residential sector. Room air conditioner sales growth in India peaked in the last few years at 20percent per year. In this paper, we perform an engineering-based analysis using data specific to Indian appliances. We evaluate costs and benefits to residential and commercial sector consumers from increased equipment costs and utility bill savings. The analysis finds that, while the BEE scheme presents net benefits to consumers, there remain opportunities for efficiency improvement that would optimize consumer benefits, according to Life Cycle Cost analysis. Due to the large and growing market for refrigerators and air conditioners in India, we forecast large impacts from the standards and labeling program as scheduled. By 2030, this program, if fully implemented would reduce Indian residential electricity consumption by 55 TWh. Overall savings through 2030 totals 385 TWh. Finally, while efficiency levels have been set for several years for refrigerators, labels and MEPS for these products remain voluntary. We therefore consider the negative impact of this delay of implementation to energy and financial savings achievable by 2030.

This paper presents efforts to date and prospective goals towards development of a modelling and analysis framework which is comprehensive enough to address the global climate crisis, and detailed enough to provide policymakers with concrete targets and achievable outcomes. In terms of energy efficiency policy, this requires coverage of the entire world, with emphasis on countries and regions with large and/or rapidly growing energy-related emissions, and analysis at the 'technology' level-building end use, transport mode or industrial process. These elements have not been fully addressed by existing modelling efforts, which usually take either a top-down approach, or concentrate on a few fully industrialized countries where energy demand is well-understood. Inclusion of details such as appliance ownership rates, use patterns and efficiency levels throughout the world allows for a deeper understanding of the demand for energy today and, more importantly, over the coming decades. This is a necessary next step for energy analysts and policy makers in assessment of mitigation potentials. The modelling system developed at LBNL over the past 3 years takes advantage of experience in end use demand and in forecasting markets for energy-consuming equipment, in combination with known technology-based efficiency opportunities and policy types. A particular emphasis has been placed on modelling energy growth in developing countries. Experiences to date include analyses covering individual countries (China and India), end uses (refrigerators and air conditioners) and policy types (standards and labelling). Each of these studies required a particular effort in data collection and model refinement--they share, however, a consistent approach and framework which allows comparison, and forms the foundation of a comprehensive analysis system leading to a roadmap to address the greenhouse gas mitigation targets likely to be set in the coming years.

The global economy has grown rapidly over the past decade with a commensurate growth in the demand for electricity services that has increased a country's vulnerability to energy supply disruptions. Increasing need of reliable and affordable electricity supply is a challenge which is before every Asia Pacific Partnership (APP) country. Collaboration between APP members has been extremely fruitful in identifying potential efficiency upgrades and implementing clean technology in the supply side of the power sector as well established the beginnings of collaboration. However, significantly more effort needs to be focused on demand side potential in each country. Demand side management or DSM in this case is a policy measure that promotes energy efficiency as an alternative to increasing electricity supply. It uses financial or other incentives to slow demand growth on condition that the incremental cost needed is less than the cost of increasing supply. Such DSM measures provide an alternative to building power supply capacity The type of financial incentives comprise of rebates (subsidies), tax exemptions, reduced interest loans, etc. Other approaches include the utilization of a cap and trade scheme to foster energy efficiency projects by creating a market where savings are valued. Under this scheme, greenhouse gas (GHG) emissions associated with the production of electricity are capped and electricity retailers are required to meet the target partially or entirely through energy efficiency activities. Implementation of DSM projects is very much in the early stages in several of the APP countries or localized to a regional part of the country. The purpose of this project is to review the different types of DSM programs experienced by APP countries and to estimate the overall future potential for cost-effective demand-side efficiency improvements in buildings sectors in the 7 APP countries through the year 2030. Overall, the savings potential is estimated to be 1.7 thousand TWh or 21percent of the 2030 projected base case electricity demand. Electricity savings potential ranges from a high of 38percent in India to a low of 9percent in Korea for the two sectors. Lighting, fans, and TV sets and lighting and refrigeration are the largest contributors to residential and commercial electricity savings respectively. This work presents a first estimates of the savings potential of DSM programs in APP countries. While the resulting estimates are based on detailed end-use data, it is worth keeping in mind that more work is needed to overcome limitation in data at this time of the project.

The U.S. Environmental Protection Agency (EPA) influences the market for plumbing fixtures and fittings by encouraging consumers to purchase products that carry the WaterSense label, which certifies those products as performing at low flow rates compared to unlabeled fixtures and fittings. As consumers decide to purchase water-efficient products, water consumption will decline nationwide. Decreased water consumption should prolong the operating life of water and wastewater treatment facilities. This report describes the method used to calculate national water savings attributable to EPA?s WaterSense program. A Microsoft Excel spreadsheet model, the National Water Savings (NWS) analysis model, accompanies this methodology report. Version 1.0 of the NWS model evaluates indoor residential water consumption. Two additional documents, a Users? Guide to the spreadsheet model and an Impacts Report, accompany the NWS model and this methodology document. Altogether, these four documents represent Phase One of this project. The Users? Guide leads policy makers through the spreadsheet options available for projecting the water savings that result from various policy scenarios. The Impacts Report shows national water savings that will result from differing degrees of market saturation of high-efficiency water-using products. This detailed methodology report describes the NWS analysis model, which examines the effects of WaterSense by tracking the shipments of products that WaterSense has designated as water-efficient. The model estimates market penetration of products that carry the WaterSense label. Market penetration is calculated for both existing and new construction. The NWS model estimates savings based on an accounting analysis of water-using products and of building stock. Estimates of future national water savings will help policy makers further direct the focus of WaterSense and calculate stakeholder impacts from the program. Calculating the total gallons of water the WaterSense program saves nationwide involves integrating two components, or modules, of the NWS model. Module 1 calculates the baseline national water consumption of typical fixtures, fittings, and appliances prior to the program (as described in Section 2.0 of this report). Module 2 develops trends in efficiency for water-using products both in the business-as-usual case and as a result of the program (Section 3.0). The NWS model combines the two modules to calculate total gallons saved by the WaterSense program (Section 4.0). Figure 1 illustrates the modules and the process involved in modeling for the NWS model analysis. The output of the NWS model provides the base case for each end use, as well as a prediction of total residential indoor water consumption during the next two decades. Based on the calculations described in Section 4.0, we can project a timeline of water savings attributable to the WaterSense program. The savings increase each year as the program results in the installation of greater numbers of efficient products, which come to compose more and more of the product stock in households throughout the United States.

This paper presents a methodology for modeling residential appliance uptake as a function of root macroeconomic drivers. The analysis concentrates on four major energy end uses in the residential sector: refrigerators, washing machines, televisions and air conditioners. The model employs linear regression analysis to parameterize appliance ownership in terms of household income, urbanization and electrification rates according to a standard binary choice (logistic) function. The underlying household appliance ownership data are gathered from a variety of sources including energy consumption and more general standard of living surveys. These data span a wide range of countries, including many developing countries for which appliance ownership is currently low, but likely to grow significantly over the next decades as a result of economic development. The result is a 'global' parameterization of appliance ownership rates as a function of widely available macroeconomic variables for the four appliances studied, which provides a reliable basis for interpolation where data are not available, and forecasting of ownership rates on a global scale. The main value of this method is to form the foundation of bottom-up energy demand forecasts, project energy-related greenhouse gas emissions, and allow for the construction of detailed emissions mitigation scenarios.