Fostering investment on energy efficient appliances in IndiaeA multi- perspective economic input-output lifecycle assessment

Energy planning in many developing Asian countries has neglected the structure of energy demand and how it is likely to evolve as development takes its path. The limited availability of energy consumption data makes it very difficult to assess the energy savings potential at end-use level. Energy planning requires the formulation of a proper modelling framework that supports the definition of energy policies. From the different approaches available, Input Output (I-O) models are particularly useful, since they allow considering different impacts that can be consistent with different energy policy options. This paper proposes a novel I-O modelling framework by introducing a bottom-up approach into an I-O model which is combined with technical data for the holistic assessment of energy efficient technologies in the residential sector, which can assist energy decision-makers of India on the appraisal of the future impacts of the current national energy saving targets. A large size platform of real data has also been gathered considering different data sources, namely the household building stock characterization, the number of operating days according to the climatic regions of India, the lifetime and the investment cost of equipment. Finally, the main results are discussed and future research opportunities are identified. © 2018 Elsevier Ltd. All rights reserved.


Introduction
In 2012 the residential sector was responsible for consuming nearly one fourth of total world electricity consumption [1]. The growing penetration rate of electric appliances in developing countries, such as India, is one of the leading causes of the current energy demand and CO 2 emissions increase. In fact, according to India's Central Electricity Authority, electricity consumption in all households was 203 TWh in 2013 [2]. In the past decade, population and energy consumption grew at a similar rate to GDP (i.e. about 7%) and it is anticipated that energy consumption will likely surpass GDP's growth rate in about 10e30 years [3]. The level of comfort in the residential sector has been reported to steadily increase along with an increasing dependence on electricity. The residential sector is at the second rank after industry with about 24% of total electricity demand in 2013e2014 [4] and appliances account for 35e40% of that demand [5]. Energy consumption has traditionally been tied to economic development. However, its environmental implications have been a driving factor for the definition of contemporary environmental policy around the world, highlighting the role of energy efficiency (EE) in the reduction of GHG emissions [6,7]. Therefore, the consequent rise in energy consumption and GHG emissions can be significantly reduced if consumers are motivated to buy energy efficient appliances [8].
The International Energy Agency anticipates that by 2030 one of the lowest cost GHG emissions abatement option in Organisation for Economic Co-operation and Development countries will come from energy efficient end-use technologies (EET) [9]. The support of EE policies can thus be seen as a cost-effective driver of energy consumption and GHG emissions reduction, while providing economical energy services in different activity sectors [10]. The need for energy autonomy and EE plays a decisive role in the economic development and the societal prosperity for worldwide [11]. EE is an important element that needs to be included in any program associated with the promotion of economic development in all the countries without increasing the level in the use of energy sources for electricity generation.
Several studies have estimated the impacts of the adoption of EET in the residential sector in India. Most of these studies are focused on household electricity consumption [12]. For instance, the Lawrence Berkeley National Laboratory (LBNL) estimated the energy savings obtained for two particular appliances used in the residential sector: refrigerators and air conditioning [13]. A study conducted by the Energy Resources Institute assessed the energy savings in the residential sector as a whole and not per appliance/ end-use [14]. Reddy and Balachandra [15] computed the implicit energy savings potential from the usage of more efficient appliances. A bottomeup analysis approach was also developed by the LBNL (see Refs. [16,13]) for the residential sector in India by considering different use categories (e.g. lighting, water heaters, television, fan, washing machine, air coolers, air conditioners, refrigerators, etc.). A similar study was also published by the World Bank [17] that uses both an end-use and a bottom-up modelling approach. Most of the studies herein reviewed present big discrepancies between the projected and the real energy potential savings per appliance/end-use and are also lacking an integrated E3S assessment.
Energy models have been extensively used to address and assess the impact of different energy policy options [18]. These models became the focus of attention of researchers in the early seventies of the twentieth century with the first oil crises. In the framework of energy systems, I-O analysis has been applied in a multitude of contexts: to estimate China's energy disparities in energy consumption [19]; to provide primary energy forecasts in the Spanish economic system [20]; to analyse the employment impacts of EE retrofit investments [21] and renewable energy targets in Portugal [22]; to account for the economic impacts of EE and renewable energy in Germany [23]; to compute direct and indirect energy use and carbon emissions in the production phase of buildings in Sweden [24]. Over the last decades, several I-O models were also coupled with other mathematically based formulations in order to enhance the understanding and prediction of future impacts of energy use. For example [25], identified strategies for mitigating the global warming impact of the European Union-25 economy by using a multi-objective I-O approach [26], optimized sectoral production with energy and GHG emission constraints in Greece and [27] assessed the E3S trade-offs in the Brazilian economic system. A review of Input-output (I-O) analysis with Multi Objective Linear Programming models for the study of E3S interactions can be found in Ref. [28]. Suitable E3S models allow assessing the impacts of market transformation in the framework of EE. I-O analysis provides a modelling approach that can be particularly useful to compute the primary energy and GHG embodied in final demand [29].
In this context, this paper presents an I-O framework instantiated with real data which provides an integrated assessment of the E3S impacts of nine energy efficient appliances currently used in India's residential sector, i.e. lighting sources (TFL), refrigerators (FR), room air-conditioners (RAC), water, electric heaters (EG), televisions (TV), computers (COM), ceiling fans (CF), water pumps (WEP) and washing machines (WM). The time horizon considered for the study herein conducted comprises 2011 to 2030 and it is assumed that the adoption of energy efficient appliances/end-uses will have started in 2011 (the reference year of the study for which the most recent data was available) and that all new appliances purchased will be energy efficient (i.e. will correspond to the BAT). Although this also means that it is not possible to benchmark the outputs of the model with real data even for past years between 2011 and the present date, the I-O modelling framework herein suggested is designed to assist planners and energy decisionmakers of India on the appraisal of the future impacts of the current national energy saving targets, providing a contribution that can help to shape future energy plans in the country.
In the next Section a description of the I-O modelling framework developed is given. Section 3 provides the main assumptions considered in order to instantiate the model. Section 4 presents a discussion of the main illustrative results obtained. Finally, some

Methodology
The E3S impact assessment of energy use of an economy in a resource-constrained world requires an understanding of the relationships between its economic, social, and energy-use elements [22].
I-O is an approach that allows capturing all the economy-wide interdependencies. Generalized I-O analysis allows obtaining total factor multipliers, which describe embodiments of production factors (e.g. labour, energy, resources) and pollutants per unit of final consumption of commodities [18].
The traditional economic I-O model is based on an I-O matrix with the economic flows between industries that can be extended with information regarding the E3S impacts, creating additional columns and rows that represent the E3S impacts per each activity sector/industry [30]. This additional information is obtained just by combining national accounts with satellite national statistics for pollutant emissions, employment and energy.
According to the traditional I-O framework, the productive system at a national level can then be represented in its matrix form following the basic I-O system of equations: where A is a matrix of technological coefficients, y is a vector of final demand (household consumption, government consumption, investment and net exports) and x is a vector of the corresponding outputs.
In order to finally obtain the output multipliers, the Leontief inverse matrix needs to be obtained. Equation (1) can then be readjusted to: where I is the identity matrix with convenient dimensions and (I e A) À1 is also known as the Leontief inverse. Each generic element of (I e A) À1 represents the total amount of good or service i directly and indirectly needed to deliver a unit of final demand of good or service j [31]. Indeed, the Leontief inverse indicates the direct, indirect and induced requirements of production that are needed to satisfy a particular final demand vector. Thus, this matrix is also known as the multiplier matrix. An approach for obtaining the E3S impacts associated with inter-industry activity consists of assuming a matrix of direct impact coefficients, R. Hence, the level of E3S impacts associated with a given vector of total outputs can be expressed as: where r is the vector of E3S impact levels. Thus, we can compute vector r as a function of final demand, i. e: Finally, from (4) we can interpret R (I e A) À1 as a matrix of total E3S impact coefficients; i.e., an element of this matrix is the total E3S impacts per monetary unit of final demand presented to the economy.
Since official published I-O data do not provide the clear identification of the E3S impacts that are likely to be created by an increase in the demand for the use of a typical BAT/BAU appliance, in the next Section of the paper we provide a thorough explanation of the approach herein suggested, by combining data on expenditures for domestic appliances with I-O modelling techniques to arrive at the related direct, indirect and induced economic, energy, environmental and social impact effects.

A new framework for assessing energy efficient appliances
In order to assess these E3S estimates, the economic impulses that originate these impacts must be identified (as in Table 1). Therefore, the lifecycle of a BAT/BAU appliance is divided into different lifecycle phases (i.e. installation and operation and maintenance -O&M) and then these phases need to be further decomposed into their corresponding activities/components. Through collecting information on the total expenditure connected to each lifecycle phase, along with data on the cost share of each relevant activity/component as a percentage of the corresponding lifecycle phase, it is then possible to calculate the total output (in monetary units) of each of these relevant activities/components.
The lifecycle phases can then be economic activities that provide impulses in the form of expenditures that can generate different E3S effects. Impulses (e.g. expenditures for O&M, manufacturing and installation of BAT/BAU appliances) are regarded as exogenously determined parameters that trigger an economic mechanism that leads to several effects. Effects (e.g. a direct positive effect could be an increase in BAT/BAU appliance production; a negative induced effect could be a decrease in the consumption of goods) relate to how impulses influence the economy e positively, negatively, directly, indirectly or induced. The most important impulses herein analysed are: investment and O&M expenditures, including impacts on upstream industries (direct and indirect effects e obtained through type I multipliers, i.e. (direct effect þ indirect effect)/direct effect); the impulse from household income due to changes in the investment on BAT/BAU appliances (obtained through type II multipliers e i.e. (direct effects þ indirect effects þ induced effects)/direct effect). Fig. 1 depicts the relationship between impulses, effects and impacts.
The methodological approach followed has various implementation steps which are provided below, being also depicted in Fig. 2.
Step 1 e Defining the system boundaries of BAU/BAT appliance industries: The BAU/BAT appliance industries include all economic activities that are related to and are characteristic for or specific to BAU/BAT appliance use. The term "BAU/BAT appliance use" comprises the complete lifecycle of the BAU/BAT appliance use, which can be roughly split into: manufacturing and operation. The life cycle consists of various activities such as manufacturing the various components needed for the BAU/BAT appliance use, O&M and replacement of parts after their defined lifetime is over (see Fig. 3).
Step 2 -Determine expenditures for the BAU/BAT appliance use: Compute the number of households for the time horizon of the analysis and obtain the appliance ownership and sales up to 2030. Estimate the number of BAT appliances needed to calculate the investment on new energy efficient appliances. Determine energy consumption during operation based on the computation of total annual energy demand with expressions (5) and (6). Obtain the technical energy savings potential with expression (7).

1) Energy Demand
2) Energy Savings per year k where EBAU k is the BAU energy demand at year k, EBAT k is the BAT energy demand at year k, NOH k is the projected number of dwellings for year k, RPA i is the rated power of appliance/end-use i, RPA(BEEh) i is the rated power of appliance/end-use i for the star labelling efficient appliances according to the BEE, OTA i is the operational time per year of appliance/end-use i and TNS i the average number of appliance/end-use of type i per household.
Step 3 -Calculate domestic output by BAU/BAT appliance technology: Distribute the expenditures to cost components which can be related to certain economic activities (see Fig. 3). Determine, at the cost component level, the import shares and subtract imports from expenditures to obtain domestic output. Allocate the domestic output for each economic activity to the appropriate industry as represented in the I-O model. Compile a vector of domestic output by industry for each lifecycle phase of each technology. Put all the vectors into a matrix of direct domestic output by industry.
Step 4 -Calculate direct, indirect and induced E3S net impacts: 1) Economic impacts The GVA is the value of output less the value of intermediate consumption and it can be seen as a measure of the contribution to GDP made by the industry sectors engaged with the BAU/BAT appliances. The computation of the net GVA has been done in two stages: Computation of the direct, indirect and induced GVA generated during the production and installation of BAT and BAU appliances.
Computation of the direct, indirect and induced GVA throughout the lifetime of the equipment due to energy consumption. The computation of the net GVA is then obtained by considering: GVA from BAT (BAU) appliances ¼ GVA during production and installation of BAT (BAU) appliances þ GVA generated throughout the lifetime of the BAT (BAU) appliances due to energy consumption. Net GVA ¼ GVA from BAT appliances e GVA from BAU appliances.

2) Environmental impacts
The computation of the net pollutant emissions has been done in two steps: Computation of the direct, indirect and induced emissions generated during the production and installation of BAT and BAU appliances. The emissions associated with the decommissioning and dismantling of the appliances have not been considered due to the lack of data sources and the absence of electronic waste management facilities in India.

3) Social impacts
The computation of net employment has been done in two steps:  Computation of the direct, indirect and induced employment generated during the production and installation of BAT and BAU appliances. Computation of the direct, indirect and induced employment throughout the lifetime of the equipment.
In this case, we consider that the employment generated during the manufacturing stage of BAU appliances will be kept with the replacement of old appliances with BAT appliances, assuming in this situation that an upgrade of the job skills is sufficient.
Therefore, the computation of the net employment is then obtained by considering: Net employment ¼ Employment from BAU appliances þ Net employment change e Employment loss. Net employment change ¼ Employment from BAT appliances e Employment from BAU appliances. Employment from BAT (BAU) appliances ¼ Employment during production and installation of BAT (BAU) appliances. Employment loss ¼ Employment due to electricity consumption with BAU appliances -Employment due to electricity consumption by BAT appliances.

Assumptions and estimates
In order to apply the I-O adjusted modelling framework to our problem, a large size data platform of real data for the residential sector in India has been gathered considering different data sources in particular: national I-O tables [32] and satellite accounts (socio economic accounts and environmental accounts) published by the world I-O database (http://www.wiod.org/) for India, the household building stock characterization, the number of operating days according to the climatic regions of India, the lifetime and the investment cost of each energy efficient equipment/measure (see Table 2, Table 3, and Table 4).
The average share of materials and corresponding costs were obtained from Refs. [33e36] e see Table 3. The investment costs used for each BAT appliances were attained from web based market research on several shopping websites and star labelling efficient appliances. Table 5 provides the projected overall investment on BAT appliances according to basic constant prices for several land mark periods. Data regarding energy consumption has been obtained from the energy balance of India and then combined with the World I-O Database.
The base year of our study is 2011 because this is the year for which validated census data is available. The time frame considered in our analysis goes from 2011 to 2030. The reason for this choice lies on the fact that it is expected that major technological changes will only take place within the next 16e20 years (see for example the replacement of cathode ray tube television by plasma display or   liquid crystal display television) [6]. In our study, we have assumed that the adoption of energy efficient appliances/end-uses will have started in 2011 and that all new appliances purchased will be energy efficient (i.e. BAT appliances), meaning that it is not possible to benchmark the outputs of the model with real data even for past years between 2011 and the present date.

Household projections
The household size is usually considered in this sort of studies and projection [8,17]. Nevertheless, our methodology is slightly different and based on the classification of households, according to the Census of India [37,38], (see Table 6).
According to the Census of India, the total number of households will reach 344,978,775 by 2030. Following Step 2 of the methodology explained in the previous Section, the annual percentage of growth rate is projected to be 2.2% by 2030 (see Table 7).

Appliance ownership and sales
In order to estimate future sales, we have used a linear regression model based on data of the sales within the period of 2007e2011. The results obtained were validated and contrasted with the data published in Refs. [6,17,39,40,41]. Data regarding the rate of appliance ownership were based on several data sources. The total share of sales to the residential sector are given in Table 8. Data regarding the rate of appliance ownership were based on several data sources (see Table 9).

Selection of appliances
We have identified twenty typical appliances/end-uses in the residential sector. However, only nine appliances are accounted for in the energy efficient star labelling appliance/end-use categories of the BEE by Government of India (see Refs. [48,49]). Therefore, our study will only focus on those nine electrical appliances/end-uses (see Table 10).
The average yearly energy consumption values considered in our analysis for BAT/BAU appliances are provided in Table 11. Table 12 shows the operating hours per year considered for each BAT/BAU appliance, which were based on [12e14, 17,41], and [71].

Discussion of results
The I-O framework herein developed allows estimating the net E3S impacts obtainable with the investment in BAT appliances within a consistent framework. Our approach combines data on expenditures for domestic appliances with I-O modelling to assess the related direct, indirect and induced E3S effects. The illustrative results regarding the E3S impact assessment of the nine electrical appliances under evaluation are presented below.

Energy consumption
Some of the impacts of avoided electricity consumption during the operation stage of the nine energy efficient appliances (BAT), as compared to the corresponding conventional ones (BAU) herein considered were computed according to the methodology followed in Section 2 and to data and assumptions provided in Sections 3 and 4. The results obtained for the time horizon considered in this study are presented in Fig. 4, where TESP stands for technical energy savings potential.
Out of the nine appliances under assessment three (TV, CF, FR) are responsible for more than 80% of the energy consumption in 2011 (see Fig. 4).
If the replacement of BAU with BAT appliances took place in the time of horizon of this study, the avoided electricity consumption would correspond to 97 TWh in 2011, 129 TWh in 2015, 194 TWh in 2020 and 326 TWh in 2030 (see Table 13).
According to our projections and to the penetration rate of the electrical appliances evaluated in 2030 a reduction is anticipated of 98.6 TWh, 74.7 TWh and 56.7 TWh of electricity consumption with the replacement BAU with BAT for TV, COM and FR, respectively (see Table 12).
A study conducted to estimate the electricity savings obtained from the replacement of RAC, FR, TV and CF BAU with BAT suggests a reduction of electricity consumption in Indian households of 165 TWh in 2030 [8]. LBNL estimated a reduction of electricity consumption for Indian households of 78 TWh in 2030 just with the replacement of RAC and FR [72]. According to our assessment the overall amount of energy savings attainable in 2030 with the replacement of BAU RAC, FR, TV and CF with BAT would be 227 TWh.
These results illustrate the tremendous impact on the energy savings if measures to promote the investment in energy efficient appliances are adopted and become effective, namely avoiding the need of installing new thermal power plants in India.

Avoided energy costs
We have computed the cumulative electricity savings and corresponding energy avoided costs by taking into account the energy savings obtainable with the energy efficient technologies (BAT) Bold values signifies the base/projected year of time -horizon.     [69,70] against conventional technologies (BAU). Fig. 5 illustrates the avoided energy costs in different time frames indicating that the investment in more efficient TV, COM and RAC offers the highest potential for reducing energy costs. Out of the nine BAT appliances considered, TV, COM and RAC (Fig. 5) are accountable for more than 80% of the expected avoided energy costs. Namely, the replacement of BAU appliances with BAT appliances corresponds to an overall avoided energy cost in million $ of about 6109 in 2011, 70,519 in 2015, 281,837 in 2020, 566,242 in 2025 and 937,349 in 2030.

Economic impacts
The anticipated direct, indirect and induced impact on GVA resulting from the investment in energy efficient appliances for the different time horizons of this study is provided below (see Fig. 6).
Out of the nine BAT appliances considered only COM, TV, CF and FR are responsible for more than 80% of the expected net GVA in 2011 (see Fig. 6). The replacement of BAU appliances with BAT appliances corresponds to a net positive direct, indirect and induced GVA of 5, 460 million $, 4406 million $, and 144 million $, in 2011, respectively (see Table 14). The positive impacts on GVA always occur in the manufacturing lifecycle stages while the negatives impacts are obtained in the operation stage. This explains the anticipated overall negative economic impact on direct, indirect and induced GVA in 2015, 2020, 2025 and 2030.

Environmental impacts
The emissions accounted for in our study include both CO 2 emission and non-CO 2 emissions and involve energy-related and non-energy related combustion, covering their corresponding global warming potential (GWP) in tonnes of CO 2 equivalent (tCO 2 eq) [73,74]. SO 2 , NO x and NH 3 are the primary gases used to account for the acidification potential [75]. The tropospheric ozone potential involves emissions of the following gases: NO x , NMVOC, CO and CH 4 [76].
The avoided GHG emissions were computed by considering that each GHG has a different GWP and persists for a different period of time in the atmosphere. The three main GHG and their 100-year GWP compared to carbon dioxide are [76]: 1 Â CO 2 ; 25 Â CH 4 (i.e. releasing 1 kg of CH 4 into the atmosphere is almost equivalent to releasing 25 kg of CO 2 ); 298 Â N 2 O (i.e. releasing 1 kg of N 2 O into the atmosphere is almost equivalent to releasing 298 kg of CO 2 ).
The calculation of the acidification potential is analogous to the GWP and is stated in SO 2 equivalents (tSO 2 eq). The chosen acidifying substances (i.e. SO 2 , NO x and NH 3 ) are aggregated into a single indicator, after assigning to each specific pollutant the corresponding acidification potential: 1 Â SO 2 ; 0.7 Â of N 2 O and NO X (i.e. releasing 1 kg of N 2 O and NO X into the atmosphere is almost equivalent to releasing 0.70 kg of SO 2 ); 1.88 Â NH 3 (i.e. releasing 1 kg NH 3 into the atmosphere is almost equivalent to releasing 1.88 kg of SO 2 ) [76].
The calculation of the tropospheric ozone formation potential  [76]. The appliances with the highest GHG reduction potential in 2011 and 2030 are COM, FR, TV, RAC, CF and TFL (see Fig. 7). If the replacement of BAU with BAT appliances took place in 2011, there would be no avoided GHG emissions in the starting year of the assessment with TV, CF, FR, corresponding to an overall increase of 2,874,234 tCO2eq in 2011. However, an overall reduction of GHG  The appliances with the highest acidification reduction potential in both years are (once more) FR, TV, RAC, CF and TFL (see Fig. 8).
If the replacement of BAU with BAT appliances was carried out in 2011, the avoided overall acidifying gas emissions would correspond to 11,908,928 tSO 2 eq. Considering the same previous assumptions, our projections also indicate that in 2030 an overall reduction of acidifying gas emissions of 2,028,770,397 tSO 2 eq is foreseen.
The appliances with the highest anticipated reduction projections for TOFP in 2011 and 2030 are TV, RAC, TFL and CF (see Fig. 9). With the replacement of BAU with BAT appliances in 2011, the avoided emissions of ozone precursors would correspond to 396,294 tNMVOCeq. Our projections also indicate that in 2030 an overall reduction of ozone precursors is predicted of 50,172,411 tNMVOCeq.

Social impacts
The foreseen overall net employment generation with BAT appliances is illustrated in Fig. 10 indicating that promoting EE will  These outcomes are aligned with the conclusions of ACEEE, suggesting that there will be a job loss due to energy efficient technology [77,78].

Conclusions and future work developments
An I-O lifecycle assessment modelling approach has been suggested which allows obtaining several projections regarding the assessment of the E3S impacts associated with the replacement of less efficient appliances (BAU) with BAT appliances from the year 2011e2030. Several environmental impacts have been covered, including GHG emissions, acidifying substance emissions and TOFP emissions. According to the scenarios considered in our analyses, we have concluded that there will be no avoided GHG emissions in the starting year of our assessment for TV, CF, FR. This outcome is obtained because the impact of the manufacturing lifecycle stage will be more significant than the operational phase in the initial stages of our analysis. However, the longer the lifetime of the equipment, the higher the expected overall reduction of the GHG emissions throughout the equipment's lifetime. According to our projections in 2030, an overall reduction of GHG emissions is anticipated of above 337,225 10 3 tCO 2 eq. The avoided acidifying gas emissions would reach more than 11,908 10 3 tSO 2 eq in 2011, whereas in 2030 an overall reduction of more than 2,028,770 10 3 tSO 2 eq is estimated. Regarding the TOFP avoided emissions these would correspond to above 396 10 3 tNMVOCeq in 2011, while in 2030 an overall reduction of more than 50,172 10 3 tNMVOCeq is expected. Our study also indicates that TV, CF, FR and RAC BAU appliances will have been responsible for more than 85% of the potential GHG emissions, in 2011. From the nine appliances herein considered, three (TV, CF, FR) alone are responsible for more than 80% of the energy consumption. In particular, the replacement of current less efficient models with BAT can reduce the overall energy consumption to 326 TWh in 2030. The cumulative electricity savings obtained with the replacement of BAU with BAT appliances, may correspond to an overall avoided energy cost in million $ of about 6109 in 2011, 70   million $ and 7, 132 million $, respectively. The overall economic effect becomes negative because of the anticipated reduction of energy consumption. Nevertheless, this conclusion is not straightforward since an expected increase of private consumption might be anticipated with the money saved in electricity consumption.
The overall net employment generation caused by the adoption of BAT appliances suggests that promoting energy-efficient appliances will lead to positive direct net job generation of 337, 1352, 2089, 2082 and 2306 thousand jobs in 2011, 2015, 2020, 2025 and 2030 respectively. Although the overall employment effect is largely positive, a negative impact on indirect and induced employment in 2030 can be anticipated.
We can conclude that the adoption of more energy efficient technology will not necessarily lead to high economic impacts. However, it is expected that the avoided energy consumption costs can be used to further promote the investment in more efficient technologies (e.g. super-efficient appliances). Future work is currently under way to encompass these aspects into our analysis as well as to couple the I-O modelling framework developed with other mathematical modelling tools to better explore the main potentialities offered by different approaches, in particular, MOLP models.