Evaluation of progress in global warming mitigation policies for the building sector in Japan

Japanese global warming countermeasures

As the basis for its PGWC, the Japanese Agency for Natural Resources and Energy had published the Long-term Energy Supply and Demand Outlook (LESDO) (Agency for Natural Resources and Energy, 2021). LESDO had established the targets for introducing energy efficiency and decarbonization technologies required to meet the PGWC goals for each sector, as shown in Table 1. The government is implementing various policies to ensure these technologies achieve the dissemination rates indicated in Table 1.

Follow-up procedure of PGWC

The progress of PGWC is reviewed every year by a joint council of the Ministry of Environment and the Ministry of Economy, Trade and Industry (Ministry of Environment 2026a). The review is done in two ways:

• Evaluation based on the National Greenhouse Gas Inventory.

• Evaluation of the progress of each of the measures shown in the last column of Table 1.

From the evaluation of progress in FY2023 (Ministry of the Environment 2025), the results were as follows.

• Japan’s national greenhouse gas emissions and removals for FY2023 were 1,017 million tonnes of carbon dioxide equivalent (Mt CO₂ eq.), showing a 27.1% decrease compared to FY2013 (National Institute for Environmental Studies 2025). This represents reductions are on track toward the 2030 targets. However, the progress of renewable electricity and restart of nuclear plants have been behind schedule, and this has been offset primarily by reduced activity in the industrial sector. As shown in Figure 1, progress toward emissions reductions in the residential sector for FY2023 is at 45%, while progress in the non-residential sector is at 58%. Assuming a linear reduction pathway, the figure should have reached 59% in FY2023, meaning the non-residential sector is almost on track, while the residential sector is behind. The residential sector’s delay can be attributed to the fact that over two-thirds of its greenhouse gas emissions come from electricity consumption, which is affected by the aforementioned decarbonization delays in the power sector. In addition, this assessment does not account for the impact of weather conditions.

• The progress of dissemination for each measure is indicated in the last column of Table 1. Despite the promotion of high-efficiency lighting has shown significant progress in both sectors, the promotion of energy efficient residential and commercial buildings in new construction has not progressed as much. In the residential sector, the adoption of high-efficiency water heaters and energy management systems is behind schedule. The adoption of high-efficiency water heaters has been delayed because they are being used longer than expected, and it is difficult to switch to a different type when they break down. The adoption of HEMS has been slow because the benefits are not easily understood by the general public.

Figure 1

The evaluations based on the two approaches differ slightly. The following reasons are considered to cause this.

• Actual CO₂ emissions and energy consumption are affected by various factors such as weather conditions and socio-economic activities.

• Since the CO₂ emission intensity of electricity significantly affects CO₂ emissions in these sectors, progress of the dissemination of energy efficiency technologies in the residential and non-residential sector does not consistently correlate with CO₂ emissions.

• In LESDO, the effect of each measure is estimated as the product of the amount of dissemination and the energy saving effect per unit. However, in reality, the magnitude of energy-saving effects of each measure varies depending on factors caused by heterogeneity, such as the characteristics of households and buildings.

Overview of the digital twin model

To improve the accuracy of the follow-up in the current state described above, the authors proposed a new carbon and energy management system suitable for the modern digital society (Shimoda et al. 2022). An overview of the system is shown in Figure 2. The model consists of the following three tools: 1) a bottom-up, end-use energy simulation model that can replicate the energy demand determination mechanism, 2) individual statistical data that explain the relationships between household/building characteristics and energy consumption, appliance ownership status, and the energy-saving behavior of a building’s occupants in order to account for the heterogeneity of households/buildings, and 3) smart meter data that can be used to analyze the energy efficiency progress of households/buildings. By combining these three tools, it is possible to assess the present situation regarding energy consumption and CO₂ emissions in the residential and non-residential sectors, evaluate the progress of the energy efficiency program currently in place, and plan for new energy efficiency measures.

Figure 2

Residential sector model

To simulate end-use energy consumption in residential sector, TREES (Total Residential End-use Energy Simulation) model was used. TREES is a bottom-up engineering model based on the time-use schedule of building occupants and a heating/cooling load calculation model. The procedure of the TREES simulation model is shown in Figure 3. TREES has previously been used to evaluate the Japanese PGWC up to 2030 (Taniguchi-Matsuoka et al. 2020) and carbon neutrality in 2050 (Shimoda et al. 2021). In this study, 0.03% of Japan’s 53 million households were randomly selected as representative households. Household attributes were randomly assigned to each representative household according to the census (Statistic Bureau of Japan, 2026). Survey on Carbon Dioxide Emissions from Residential Sector (Household CO₂ Statistics), published annually by the Ministry of the Environment (Ministry of Environment, 2026), was used to provide input data such as the appliance ownership status for our bottom-up simulation. Smart meter data was used to calibrate the appliance operation schedule (Uchida et al., 2025).

Figure 3

Non-residential sector model

The energy model for the non-residential building stock uses an approach similar to the TREES model, as shown in Figure 4. The model consists of reference building models that represent a building stock segment, classified by climate condition, building business category and size, insulation performance, system configuration for space heating and cooling and water heating, and energy conservation measures. This reference building models were used for EnergyPlus simulation that would allow a quantification of energy use intensity (EUI) per floor area. (EnergyPlus, 2026) Total energy consumption was quantified by summing the products of the EUI and total floor area for all the building stock segments. The model was updated to consider the yearly change in building stock in order to quantify the yearly change in CO₂ emissions (Yamaguchi et al., 2024). There are no comprehensive government statistics available for non-residential buildings. However, the Japan Sustainable Building Consortium (2020) publishes its Database for the Energy Consumption of Commercial Buildings (DECC), which provides data on the energy consumption, building use, and building size for 44,000 buildings. This data was used to validate our simulation model.

Figure 4

Residential sector

The annual changes in end-use energy demand in the residential sector were compared between simulation results and statistical data (Agency for Natural Resources and Energy 2026), expressed as reductions relative to 2013 energy consumptions. In this simulation, estimation model for the energy efficiency distribution of housing stock (Taniguchi et. al. 2008) is used. Dissemination ratio of LED lighting and high efficiency water heaters are obtained from government survey data (Ministry of Environment 2025). Energy efficiency distributions of TV, refrigerator and room air conditioner are estimated from Household CO₂ Statistics (Ministry of Environment 2026b).

Figure 5 shows the reduction in end-use energy consumption for the residential sector from FY 2013 to FY2023, broken down by factor, as obtained by the model. From this result, the following can be observed.

Figure 5

• The simulated energy consumption reductions show good agreement with statistical values for all years except FY2017 and FY2020. The gap in FY2020 is likely due to increased occupant time spent at home during the pandemic, which the simulation did not consider. The cause of the FY2017 gap is unclear, the fact that the gap is significant only in specific region suggests there may be some issue with the statistical values.

• The impact of weather differences was significant until FY2019, contributing to the apparent steady reduction in greenhouse gas emissions shown in Figure 1. After FY2020, its proportion has decreased. The effect of weather differences has consistently been positive, indicating that the weather in FY2013 led to a significant increase of residential energy demand.

• The primary contributors to reduced energy consumption are heating and cooling—namely, improvements in building energy efficiency and room air conditioner efficiency—along with water heating and refrigerators. The full mandatory implementation of energy efficiency standards for residential buildings begins in 2025; since water heaters tend to be used for longer than anticipated, compliance with energy efficiency standards for newly built homes and the adoption of high-efficiency water heaters are behind schedule. However, since their energy reduction potentials are significant, strengthening these measures are very important. Refrigerators have seen rapid efficiency improvements since around FY2000 due to the introduction of top-runner standards, with their effects lasting even 20 years later.

• Excluding the effects of different weather conditions from the results of the simulation, end-use energy reduction is 15% smaller than the planned value in 2023.

Non-residential sector

This sector is classified as “Commercial and Other” in Japan’s energy statistics, which also includes energy consumed by water supply and waste management industries, as well as statistical errors. The authors exclude these non-building sectors—specifically ‘Electricity, Gas, Heat Supply and Water’; ‘Transport and Postal Activities’; ‘Miscellaneous Services’; and ‘Unable to classify (statistical errors)’—and analyse the remainder as non-residential building energy consumption. One-quarter of the GHG emission reduction in the non-residential sector shown in Figure 1 is attributed to reductions in the ‘Unable to Classify’.

In the non-residential sector model, specifications for lighting, HVAC equipment, and other systems are established based on building use and age, and data on building demolition and new construction are used to determine the overall distribution of thermal insulation levels and the composition of equipment types and efficiency across the entire building stock. Details are presented in Yamaguchi et. al. (2024).

Figure 6 shows the simulation results until FY2021 and the result of government’s assessment, namely the product of observed dissemination amount and energy use reduction per unit (Ministry of Environment 2025). This simulation result excludes the effects of annual weather variations and energy use of non-building sectors. In the non-residential sector, the impact of weather variations is not as significant as it is in the residential sector. (Yamaguchi, et al. 2024) Due to difficulties in obtaining data, simulations beyond 2022 could not be conducted.

Figure 6

• The simulated energy consumption reduction is 20% higher than governmental evaluation. However, 25% of the energy savings are offset by the increase in floor area, which is not considered in the governmental evaluation.

• The most significant effects come from energy efficiency improvements in the building’s HVAC system and lighting, highlighting the importance of measures taken during both new construction and renovation.

Prediction of electricity consumption and CO₂ emission associated with fuel consumption

Using the simulation results described in the previous section, greenhouse gas emissions for both sectors in FY2030 were predicted. The reduction in annual energy consumption calculated by simulation is nearly linear in both sectors; accordingly, this trend was linearized and extrapolated through fiscal year 2030.

First, energy consumption is divided into electricity consumption and fuel consumption, and the annual changes in each energy use are subjected to regression analysis. This implies that not only future energy savings but also fuel switching will maintain the trend observed previously. This serves as a business-as-usual measure in promoting electrification policies.

Next, the actual greenhouse gas emissions from both sectors in FY2013 are decomposed into those attributable to electricity consumption and those attributable to fuel consumption. The above trends are then applied to each component to forecast greenhouse gas emissions for FY2030.

It should be noted that the 2030 projections presented here are based on the simulation results from the previous sections, which calculate energy consumption based on 2013 weather conditions.

Figure 7 shows the comparison between the estimated electricity consumption obtained from the simulation up to FY2030 and the actual electricity consumption. In the residential sector, actual electricity consumption since FY 2020 has been higher than the estimated values due to the impact of the pandemic in 2020 and the increasingly hot summers from FY2023. In the non-residential sector, electricity consumption other than building is excluded, the discrepancy with the observed values is slightly larger due to discrepancies in the allocation of electricity and fuel, as well as differences in weather conditions. For both sectors, the projected electricity consumption in 2030, excluding weather effects, will be lower than the LESDO target value.

Figure 7

Figure 8 shows the comparison between the estimated CO₂ emissions due to fuel consumption obtained from the simulation up to FY 2030 and observed emissions due to fuel consumption. For non-residential sector, emissions of ‘Other sectors and statistical error’ described above are added to simulation result.

Figure 8

In the residential sector, the simulated emission trend is always larger than the observed one due to warmer winters. Estimated emission in FY2030 is 2.8 times larger than the target. However, by FY2030, winters are expected to become warmer, and fuel consumption and associated CO₂ emissions are likely to be lower than predicted. Since our model can account for changes in temperature, energy consumption that considers the effects of global warming can be predicted if an appropriate temperature rise trend is specified.

In the non-residential sector, the significant reduction until FY2023 is primarily due to the reduction of ‘statistical error’. The reduction rate thereafter is expected to slow considerably, as it will be driven solely by the actual reduction in buildings.

Prediction of CO₂ emissions from the building sector

From the results obtained in the last section, CO₂ emissions from the building sector in FY2030 is estimated. LESDO has set the CO₂ emission intensity for electricity consumption in FY2030 at 0.25 kg-CO₂/kWh. However, decarbonization in the power sector is currently delayed, with the latest figure for FY2023 standing at 0.458 kg-CO₂/kWh. Therefore, we have projected CO₂ emissions for the non-residential and residential sectors in FY2030 based on several electricity CO₂ emission intensities. The values for the “Other” category in the non-residential sector are assumed to remain unchanged from the 2023 values. The results are shown in Table 2.

Thus, while the non-residential sector is expected to achieve the government’s targets, the residential sector will fail to meet its goals if the CO₂ emission intensity is 0.25 kg-CO₂/kWh. Since both sectors meet their targets for electricity consumption, this indicates that electrification in residential sector has not progressed as planned. In reality, achieving the PGWC target is increasingly difficult for both sectors due to delays in renewable energy dissemination and the restart of nuclear power plants, which will result in higher CO₂ emission intensity as shown in the Table 2.