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Abstract
The global economic recovery and geopolitical events have significantly disrupted energy markets, leading to substantial increases in energy prices worldwide. This article focuses on the impact of these price increases on vulnerable households in Kobe, Japan, using the fuel poverty index. It examines the unique challenges faced by households in warm climates, where energy consumption is driven by both heating and cooling demands. The study uses a comprehensive questionnaire survey and statistical analysis to identify the characteristics of fuel-poor households and assess the effectiveness of existing policy measures. By combining macro-level data with micro-level household consumption data, the research provides a detailed and realistic analysis of energy poverty. The findings highlight the need for targeted policy interventions to support vulnerable households and ensure energy affordability. The proposed methodology is simple and practical, making it applicable to various contexts and regions. The article concludes by offering policy recommendations to mitigate energy poverty and promote energy justice and equity.
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Abstract
The global economic recovery from the COVID-19 pandemic and the Russian invasion of Ukraine have significantly increased global energy prices, resulting in higher household burdens for citizens. This study uses fuel poverty indicators to determine household vulnerability to increase in energy price in Japan. A web questionnaire was conducted asking about household income and fuel bills in 2021 and 2022, using Kobe City, Japan, as a case study. As the result, the percentage of fuel-poor households in 2022 was 5.39%, an increase of 0.23% over 2021. This suggests that fuel-poor households are increasing in response to increasing energy prices. The 2022 fuel poverty rate for elderly households was 1.48%, and it was 3.91% for households other than the elderly. Analysis result regarding the characteristics of households determined to be fuel-poor indicated that single-parent households were more likely to fall into fuel poverty than average households. A model combining binomial logistic regression and multiple regression analyses was derived to measure the effect of fuel poverty measures based on the questionnaire survey results. The study results of measures to reduce the time spent using climatization equipment during the daytime in summer by implementing energy sharing showed that all households and single-parent households could move out of fuel poverty. However, elderly households could not move out of fuel poverty, and an additional condition for breaking out of fuel poverty was an increase in the annual household income of elderly households by 0.2 million JPY (Japanese Yen).
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Introduction
The global economic recovery from the COVID-19 pandemic that began in 2020 and the Russian invasion of Ukraine in February 2022 significantly disrupted the global energy supply demand balance that had been in place before 2020. This resulted in a significant increase in global energy prices; in the case of crude oil, the West Texas Intermediate (WTI) rose from 17.66 USD per barrel1 in April 2020 to 114.59 USD per barrel in June 2022, according to the World Bank (2024). for natural gas, the Title Transfer Facility (TTF) rose from 1.58 USD per million British thermal unit (MMBtu)2 in June 2020 to 70.04 USD per MMbtu in August 2022. This situation has led to higher electricity prices in many countries worldwide (Kosch et al., 2022).
Higher energy prices accompanied by inflation lead to increase in household spending on energy (Bolton, 2010; Keček, 2023; Sumantri, 2022). Guan et al. (2023) conducted a scenario analysis of the rate of increase in energy prices from February to September 2022 compared to the average energy prices in 2021 for 116 countries. According to their study results, they claim that the total energy bills of households would increase by 62.6–112.9%, contributing to a 2.7–4.8% increase in household expenditures worldwide. They also indicate that an additional 78–141 million people could potentially be pushed into extreme poverty under cost-of-living pressures. Fuel poverty is a concept that refers to households that spend a large proportion of their household income on heating fuel bills (electricity, gas, etc.) (Boardman, 2010). Hinson et al. (2024) estimate that 13.0% of all households in England will be in fuel poverty by 2023.
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In particular, households belonging to socially vulnerable groups, such as the elderly and single-parent households, are more vulnerable and at a higher risk of being affected by increase in energy price. Fuel poverty is often confused with the term energy poverty, but former focuses on energy consumption in the housing sector, and low income tends to exacerbate the depth of both fuel poverty and energy poverty, impairing health, undermining equity, and hindering the proper development of society (Li et al., 2014). It has been argued that the issue of energy poverty in households depends on both “accessibility” as a supply of energy infrastructure and “affordability” as the securing of energy at an affordable price (Bouzarovski & Petrova, 2015).
In a case of Japan, the national electricity prices in December 2022 were 58.5% higher than those in December 2020 (Energy Information Center, 2024). However, although Japan has implemented measures to liberalise the electricity sector, there are still electricity companies with a monopoly status in each region. These power companies have different power price increases due to their different power supply mix. Although a monopolistic position means that competition is incomplete, it has contributed to ensuring the accessibility of electricity for residents. In response to rising energy prices and inflation, the affordability of electricity for residents has declined. Japan has implemented schemes such as subsidies for low-income households and controls on the wholesale price of heating oil (Japan Cabinet Office, 2022; Japan Agency of Natural Resources and Energy, 2024). These measures are considered to have limited the rise in energy prices compared with other countries that did not implement similar measures.
In Japan, there is no institutional design linking energy to well-being, as is the case with fuel poverty. According to Köppen's climate classification, Europe is divided into three major climatic zones: Marine west coast climate, Mediterranean climate, and Cold zone humid climate. The concept of fuel poverty is based on European countries where Winters are colder in the Marine west coast climate zone such as United Kingdom. However Sánchez et al. (2017) pointed out that attention should also be paid to fuel poverty during summer. Studies on fuel poverty and/or energy poverty are also being conducted in warm regions such as the Mediterranean climate zone of European countries such as Portugal, considering the use of air conditioning in summer (Energy Poverty Advisory Hub, 2023; Gouveia et al., 2019; Horta et al., 2019; Panão, 2021). Our previous research estimating fuel-poor households based on summer fuel consumption in Japan found that fuel-poor households accounted for about 0.93% of all households, but this rises to 5.53% when restricted to poor households (Tabata & Tsai, 2020). Elderly households were more likely to fall into fuel poverty because they pay a higher share of their household income for electricity. The poverty rate in Japan in 2018 was 15.7%, and this figure is the second highest among G7 countries after the United States (Organization for Economic Cooperation and Development (OECD), 2024b). Households with high energy price vulnerability should be rectified in the context of energy justice and equity (Scheier & Kittner, 2022; Sovacool et al., 2023). In addition, in the analysis of the impact of equity, it is necessary to look at policy mechanisms that have both high levels of policy effectiveness and social acceptability (Sovacool et al., 2023). the use of fuel poverty indicators can be useful in analysing the characteristics of households with high vulnerability to energy prices and in designing institutions to avoid this situation (Chalier and Legendre, 2021). Such measures are limited to households with incomes below the income limit designated by the government or local governments. On the other hand, Tabata and Tsai (2020) reported that there are households that fall into fuel poverty even if they do not fall under the income limit by using the fuel poverty index. Considering this result, the current measures of the government and local governments are not able to help such households.
Many analyses of the impact of increase in energy price on prices have been conducted using macro data such as economic indicators and input–output tables (Čermáková & Hromada, 2022; Kilian & Zhou, 2022; Kosch et al., 2022; Yagi & Managi, 2023). These studies use industry input–output tables and government statistics at the macro level, and it is thought that there are limitations to the analysis of household consumption at the micro level in detail. In contrast, the fuel poverty index can empirically analyse the relationship between household consumption and energy at the micro level. Fuel poverty, sometimes referred to in the context of energy poverty, has been the subject of many studies to characterize households with high household vulnerability and to examine countermeasures. Belaïd and Flambard (2023), Das et al (2022), Dogan et al (2021), Lyra et al (2022), and Pillai et al (2022) analyse the characteristics of fuel poverty in their respective study areas. Kahouli and Okushima (2021) and von Platten (2022) discussed the importance of considering geographical and climatic conditions when assessing energy poverty. Kahouli (2020) and Tu et al. (2022) analysed the relationship between fuel poverty and well-being and found that they are causally related. Cadaval et al. (2022) and Simshauser (2023) analysed the mitigating effect of energy price reduction policies on fuel poverty. Reaños (2021) analysed the impact of a carbon tax on the incidence of fuel poverty. Li et al. (2014) indicate that fuel poverty mainly occurs in relatively wealthy countries in cold regions, but it can also occur in Spain (Gómez-Navarro et al., 2021), Greece (Lyra et al., 2022), and Japan (Tabata & Tsai, 2020), where the summer heat is severe. In this context, it is necessary to research a fuel poverty index that combines winter heating demand and summer cooling demand in regions with a predominantly warm climate such as Japan.
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In order to quantify fuel poverty and to be involved in policy, multidimensional indicators have been proposed, considering the social and economic factors that induce fuel poverty. Martín-Consuegra et al. (2020) applied this method to poor areas in Spain and related energy demand to the level of schooling, unemployment rate, and factors that limit access to housing, such as poor condition of buildings, and factors that limit access to housing. Reaños et al. (2024) reported that low overall household income is strongly associated with a higher prevalence of subjective measures of fuel poverty, a lower likelihood to engage in some energy cost mitigation strategies, and the likelihood to see administrative challenges as insurmountable barriers in the retrofit process. Olang et al. (2018) used a multidimensional index to establish the severity of energy poverty among low-income households in Kenya, and reported that energy poverty is determined by accessibility to the energy source of choice. Shabbir et al. (2024) suggest that in their survey in Pakistan, household energy demand is directly correlated with the type of fuel used, the degree of dependence on renewable energy, and the cost. Shabbir et al. (2024) also report that energy-poor households are more likely to suffer from respiratory diseases, have higher medical costs, drop out of school, and have fewer employment opportunities. In recent years, the EU has been discussing mainstreaming measures to mitigate the impact of climate change policies on fuel poverty (Vandyck et al., 2023). the need for assessments that combine environmental and energy policies with fuel poverty is shared across the field. For example, it has been reported that changes in fuel poverty due to the implementation of climate change policies can be tracked by constructing multidimensional indicators that include energy efficiency (Reaños & Lynch, 2022). In Japan, it has been reported that it is possible to identify groups that are highly vulnerable to climate change policies by capturing the region, climate, energy infrastructure, and socioeconomic characteristics of households (Castaño-Rosa & Okushima, 2021). Alkire and Foster (2011) propose a method for measuring poverty using multidimensional indicators such as health, education, work, living standards, and empowerment. In addition, multidimensional indices are often used in research related to fuel poverty. This allows for a detailed analysis of the mechanisms underlying fuel poverty, considering socioeconomic characteristics.
Analysis using multidimensional indices is technically excellent. However, complex research methods and models such as multidimensional indices are not suitable for policy evaluation by national or local governments. For policy evaluation, it is necessary to develop easy-to-understand models by selecting concise and intuitively understandable indices, rather than constructing complex analytical models.
This study aims to develop a simple methodology to identify households with high vulnerability to increase in energy price and to provide policy recommendations to avoid this situation using Japan as a case study. Using the fuel poverty index, this study empirically analyses the incidence of fuel-poor households in the wake of such strikes. We also propose countermeasures for households vulnerable to energy price hikes and measures their effectiveness. In addition, this study also focuses not only on the impact of winter heating but also on the impact of summer cooling considering the characteristics of the Japanese climate. Our study uses micro data on household consumption for each household and has the advantage of analysing the effects of increase in energy price in a precise and realistic manner. This study develops a model that can be used to evaluate the vulnerability of households to changes in energy prices from the perspective of fuel poverty, as well as to evaluate policies that ensure affordability for households with high levels of vulnerability. In addition, this study attempts to enable national and local governments to use this model to evaluate fuel poverty mitigation policies. From this perspective, this study is novel and highly significant in theses contexts. The framework proposed in this study is simple and highly practical. Therefore, this framework can be applied in countries and regions where it is difficult to obtain a large amount of socio-economic data. In addition, by using the framework of this study, it helps to evaluate the fuel poverty policy considering the characteristics of the country or region.
Materials and methods
Case study area
Figure 1 shows the target area of this study, Kobe, Japan, which has a total land area of 557 km2. The latitude and longitude of Kobe are 34° 41′ 0’ N and 135° 10′ 0’ E respectively; the average outside air temperature was 18.0 °C in 2023, with a maximum of 37.0 °C, and a minimum of −2.9 °C (Japan Meteorological Agency, 2024). Kobe City belongs to the warm and humid climate category in the Köppen climate classification. Because of this climatic division, hardly any households in Kobe City have central heating. The majority of households have air conditioning in individual rooms. The current energy efficiency standards for housing in Japan are based on the Building Energy Efficiency Act, which was enacted in April 2016. The energy efficiency standards in this law divide Japan into eight regions based on climate classification, etc., and sets the standard values for energy efficiency in each region (Japan Ministry of Land, Infrastructure, Transport and Tourism, 2016). In the case of Kobe City, the average outer shell heat transfer coefficient is 0.75 W/(m2·K) and the average solar heat gain coefficient during the cooling period is 3.0 (Institute for Built Environment & Carbon Neutral for SDGs, 2017). However, according to the 2018 Housing and Land Survey, only 3.5% of the houses in Kobe City were built after the above law came into effect (Japan Ministry of Internal Affairs & Communications, 2019). In 2018, The average floor area of houses in Kobe City was 77.86 m2, which is smaller than the average floor area of houses in Japan (93.04 m2) (Japan Ministry of Internal Affairs & Communications, 2019).
An estimated population of 1.53 million in 2020 (Kobe City government, 2024). Kobe is part of the Greater Osaka region, the second largest metropolitan region in Japan. Approximately 29.2% of the population is over 65 years of age in 2020 (Kobe City government, 2024), which is slightly higher than the national Fig. (28.6%) (Japan Cabinet Office, 2024). The electric power company mainly used by the residents in the Kobe City is Kansai Electric Power Co. The unit price of electricity for residential use by Kansai Electric Power Company was 21.43 JPY/kWh as of December 2021 and 28.06 JPY/kWh as of December 2022 (Energy Information Center, 2024). The unit price of electricity increased by approximately 31% from 2021 to 2022.
The methodology used in this study is explained in the following sections. First, a questionnaire survey is conducted to obtain information on the income, energy bills, living conditions, etc. of the residents of Kobe City. Then, the number of fuel-poor households is calculated using the results of the questionnaire survey, and their characteristics are analyzed using statistical analysis. Then, we use binomial logistic regression analysis and multiple regression analysis to create a model for analysing the impact of local government energy policies on the occurrence of fuel-poor households. This model is then used to simulate the effects of policy implementation.
Questionnaire survey
A questionnaire survey was conducted to determine actual household income and fuel bills as a percentage of household income. The questionnaire survey was conducted targeting residents of Kobe City, Japan. The survey was commissioned to JMA Research Institute Inc. in the case of elderly households and Macromill Inc. in the case of households other than elderly. Respondents owned by these companies and living in Kobe City answered questions online using PCs and smartphones. All procedures used in this research were approved by the Ethical Committee of Kobe University.
Table 1 summarizes the survey results. The survey was conducted at two points in 2021 and 2022 to investigate the impact of recovery from the COVID-19 pandemic. The study also assumed that elderly and non-elderly households would have different impacts on increase in energy price. This was set based on the fact that the average income of elderly households in Japan is only half that of non-elderly households. Tabata and Tsai (2020) also found that elderly households were more likely to fall into fuel poverty than non-elderly households.
Table 1
Summary of questionnaire survey
Survey period for household income and fuel bills
Jan-Dec 2021, Jan-Dec 2022
Surveyed area
Kobe City, Japan
Targeted Respondents
Respondents aged 65 or older (elderly households) and respondents aged 20 to 65 (non-elderly households). Respondents who lived in Kobe City during the survey period and had data showing monthly fuel bills
Table S1 (see Supplementary Material) summarizes the questions. Respondents were asked to provide household income and fuel bill values in 2021 and 2022. The 2022 survey will answer all other items. Fuel bills were divided into electricity, gas, kerosene, and others, and the respondents were asked to report their bills from January to December. To reduce the burden on respondents, in the case of the 2021 survey, respondents were asked to indicate how much higher (or lower) their fuel bills would be in 2021 compared to 2022. The same is true for household income and financial assets in 2021. Household income, household financial assets, and fuel bills were provided in Japanese currency (JPY).
These responses were then used to estimate the percentage of fuel-poor households in the survey sample and the characteristics of fuel-poor households were also analyzed. Finally, measures to help households classified as fuel-poor to escape fuel poverty were presented, and the effectiveness of implementation and challenges were measured and discussed.
Analysis of survey results
The analysis follows these steps:
1)
Calculation of the number of fuel-poor households
The number of fuel-poor households for each of the elderly only and other households was calculated, and the differences between the two types of households were analyzed. This allowed us to examine the impact of energy price increases on changes in the number of fuel-poor households. We then analyzed whether each of the basic attributes affected the incidence of fuel-poor households. The factors contributing to the incidence of fuel-poor households in each of these households were identified.
According to the result of categorization of the indicators for calculating fuel poverty by Brabo-Catala et al. (2024), many studies uses multiple indicator to evaluate results (Table S2, see Supplementary Material). In this study, the 10% and Low Income High Costs (LIHC) indicators were applied. the 10% indicator was calculated as the ratio of energy bills to total income, and anything above 10% was considered fuel poverty. Boardman (2010) uses the 10% indicator. The income threshold of the LIHC was 60% of the median annual household income, as adopted in the United Kingdom and other EU countries (Poverty and Social Exclusion, 2023). As with other indicators, the use of the 10% indicator and the LIHC indicator has many criticisms (Herrero, 2017). On the other hand, many previous studies have used these indicators (Brabo-Catala et al., 2024). The use of these indicators enables easy comparison of results. They also have the advantage of being relatively simple indicators to calculate and can easily identify fuel poor households, making them easy to apply in national and local governments’ policies (Department of Energy & Climate Change, 2013). However, we should be careful about setting the income threshold at 10% (Herrero, 2017). This study adopts 10% for ease of comparison, but future studies require searching for the optimal threshold in Japan and in each country.
2)
Measuring the effectiveness of fuel poverty measures
Based on the results obtained, a binomial logistic regression equation was created with the occurrence of fuel-poor households as the explained variable and the survey responses as the explanatory variable. Next, we propose methods for the elimination of fuel poverty in households, and simulate how these measures affect the determination of fuel poverty.
Results and discussions
Statistical analysis of survey results
Table 2 summarizes the results of the 2021 and 2022 surveys. Welch’s t-test was used to test for differences in the results between the elderly and non-elderly households. Welch's t-test is a method for comparing two groups, and is the best statistical practice that allows for the standard deviation of the two groups to differ (West, 2021). the average annual fuel bill (2021) and average number of climatization equipment were not significant. the other items such as average annual household income (2022) and average annual household income (2021) were significant at the 1% level. The interpretation of the results is that elderly households have a lower average annual household income than non-elderly households but also have more average household financial assets. The average number of climatization equipment devices did not differ between the elderly and non-elderly households in the 2021 and 2022 surveys. However, the average temperature settings of climatization equipment differed between the elderly and non-elderly households.
Table 2
Descriptive statistics of Survey results
(1) Elderly households
(2) households other than the elderly
(1) + (2)
Mean of the difference between (1) and (2) mother means
Number of respondents
250
1,030
1,280
-
Average age (respondents) [years old]
71.94
44.45
49.82
-
Average number of persons in household [persons]
1.75
2.55
2.40
-
Average annual household income (2022) [million JPY]
3.75
6.57
6.02
p < 0.01
Average annual household income (2021) [million JPY]
3.77
6.74
6.13
p < 0.01
Average household financial assets (2022) [million JPY]
23.07
13.64
15.60
p < 0.01
Average household financial assets (2021) [million JPY]
22.94
14.18
16.15
p < 0.01
Average construction date [years ago]
34.03
25.21
27.08
p < 0.01
Average years of residence [years]
22.14
11.84
13.64
p < 0.01
Average total floor area [m2]
87.62
68.08
72.26
p < 0.01
Average annual fuel bill (2022) [JPY]
158,204
165,745
164,272
p < 0.01
Average annual fuel bill (2021) [JPY]
141,694
152,607
150,475
0.3246
Average annual electricity bill (2022) [JPY]
102,020
90,685
92,899
p < 0.01
Average annual electricity bill (2021) [JPY]
104,726
96,368
98,030
p < 0.01
Fuel bills as % of annual household income (2022 survey)
5.58%
4.31%
4.56%
p < 0.01
Fuel bills as % of annual household income (2021 survey)
5.16%
4.45%
4.59%
p < 0.01
Average number of climatization equipment [units]
2.99
2.96
2.02
0.8207
Average temperature setting of climatization equipment (summer) [°C]
26.99
25.80
26.04
p < 0.01
Average temperature setting of climatization equipment (winter) [°C]
22.79
23.58
23.42
p < 0.01
Ratio of households in fuel poverty
Table 3 shows the results for the number of fuel-poor households using the 10% and LIHC indicators, and the results obtained from the questionnaire. 6.33% and 6.41% of all respondents in the 2021 and 2022 surveys fell into this category.
Table 3
Results for each indicator in the calculation of the number of fuel-poor households
2022
2021
10%
(1) Elderly households
19 (1.48%)
18 (1.41%)
(2) Households other than the elderly
63 (4.92%)
63 (4.92%)
(1) + (2)
82 (6.41%)
81 (6.33%)
LIHC
(1) Elderly households
85 (6.64%)
81 (6.33%)
(2) Households other than the elderly
196 (15.31%)
177 (13.83%)
(1) + (2)
281 (21.95%)
258 (20.16%)
Combination of 10% and LIHC
(1) Elderly households
19 (1.48%)
17 (1.33%)
(2) Households other than the elderly
50 (3.91%)
49 (3.83%)
(1) + (2)
69 (5.39%)
66 (5.16%)
In the case of the 2021 survey and 2022 survey, LIHC set the median household income of 60% to 2.70 million JPY. In the combined 10% and LIHC results, the share of fuel-poor households in 2022 was 0.23% higher than in 2021. This suggests that fuel-poor households are increasing in response to increasing energy prices.
The calculated percentage of households in fuel poverty is compared with figures for European countries: the percentage of households in fuel poverty in the England is 13% in 2023 and that Scotland will have 31.0% in 2020 (Hinson et al., 2024). The same report predicted an increase to 14.4% by 2023, highlighting the impact of high energy prices. Statistics from the European Union (2024) show that the percentage of people in 27 EU countries who could not secure adequate heating in winter increased from 6.9% in 2021 to 9.3% in 2022. However, it is necessary to be aware of the fact that, for example, it has been suggested that changing the indicator from 10% to LIHC reduces the number of fuel poverty households (Middlemiss, 2017; Robinson et al., 2018). It is necessary to check which indicator is used to calculate fuel poor households.
In Kobe City, the percentage of fuel poor households is increasing from 2021 to 2022, but the ratio of fuel poor households is extremely small compared to figures in the United Kingdom and other countries. There could be various reasons for this, one of which is the difference in climate. As explained in the introduction, Europe's climate is divided into three main categories. The United Kingdom belongs to the marine west coast climate, and the average annual temperature in London, located in the south of the United Kingdom, is 10.8 °C (Climate Data, 2024). As explained in 2.1, Kobe City has a different climate classification than London and a higher average annual temperature than London. The World Health Organization (2018) recommends that for countries with temperate or colder climates, 18 ˚C has been proposed as a safe and well. In this context, the United Kingdom has a higher demand for heating than Kobe City, which contributes to the incidence of households in fuel poverty. In terms of household income, the average annual wage (purchasing power parity (PPP) converted) in 2022 is 58,941 USD in the United Kingdom and 43,228 USD in Japan (OECD, 2024a). household income is more generous in the United Kingdom than in Japan. On the other hand, the unit cost of electricity for household use in 2023 is 0.410 USD/kWh in the United Kingdom and 0.216 USD/kWh in Japan (Global Petrol Prices, 2024). Although household income is high in the United Kingdom, the high unit cost of electricity is thought to be a factor in fuel poverty.
Most of France, like the United Kingdom, has a west coast maritime climate, and Legendre and Ricci (2015) report that 7.76% of the French population is in fuel poverty based on the results of a 2013 survey. one possible reason for the lower percentage of fuel poverty in France compared to the United Kingdom is that the unit cost of energy for households is lower than in the United Kingdom. In the case of Spain (Barcelona), which has a Mediterranean climate, the percentage of fuel poor households is reported to be 11.9% for the 10% indicator and 10.2% for LIHC (Gómez-Navarro et al., 2021). In the case of Greece, 4 out of 10 households face fuel poverty and 2.5% of all households are reported to be extremely vulnerable to fuel poverty (Lyra et al., 2022). Recognizing that countries with a Mediterranean climate have higher average annual temperatures than the United Kingdom, energy consumption to avoid extreme heat is likely a trigger for fuel poverty, as pointed out by Sánchez et al. (2017). In Japan, the impact of increased summer cooling demand on fuel poverty has also been reported (Castaño-Rosa & Okushima, 2021; Tabata & Tsai, 2020). In particular, the mechanism of fuel poverty in regions such as Southeast Asia, which is close to the equator and has high year-round air conditioning demand, could be different from that in Japan and the United Kingdom. Analysis targeting such regions is required.
Characteristics of households in fuel poverty
Table 4 shows the results of testing the differences between fuel-poor and other households using Welch's t-test to examine the characteristics of households judged to be fuel-poor. No significant differences were found for items related to the basic attributes of the respondents, such as gender and never-married status. While Tabata and Tsai’s (2020) results show differences between elderly and non-elderly households, the present results show no differences. One potential reason for the lack of significant results for elderly households in the current results is that elderly households have more financial assets than households other than the elderly. The analysis of the impact of having financial assets other than income on fuel poverty is an area for future research. However, the proportion of single-parent households was significantly higher at the 5% level in fuel-poor households. When the data were re-counted for single-parent households only, 23 respondents belonged to single-parent households. Three single-parent households (13.04% of the total number of single-parent households) were classified as fuel-poor. This percentage is 2.4 times higher than the 5.39% of fuel-poor households in the 2022 survey. Although additional research is needed due to the small number of respondents from single-parent households, it appears that single-parent households are more vulnerable to increase in energy price.
Table 4
Characteristics of fuel-poor households
Fuel-poor households
households outside fuel poverty
p value
Gender (1: male, 2: female)
1.52
1.43
0.1325
Marriage status (1: never married, 2: married)
1.54
1.60
0.4329
Children (1: no, 2: yes)
1.56
1.49
0.3585
Average age [years old]
51.31
48.99
0.3216
Average number of persons in household [persons]
2.46
2.38
0.6256
Percentage of households aged 65 and over
0.28%
0.19%
0.1328
Percentage of single parent households
0.060%
0.018%
p < 0.05
Average annual household income (2022) [million JPY]
142.03
634.83
p < 0.01
Average annual household income (2021) [million JPY]
136.74
643.49
p < 0.01
Average household financial assets (2022) [million JPY]
709.18
1,634.45
p < 0.01
Average household financial assets (2021) [million JPY]
803.91
1,675.90
p < 0.05
Average construction date [years ago]
28.63
26.69
0.2514
Average total floor area [m2]
71.64
65.62
0.1945
Average years of residence [years]
14.89
13.15
0.1943
Average number of climatization equipment [units]
2.96
2.96
0.9564
Number of climatization equipment purchased in the past year or less [units]
0.09
0.21
p < 0.01
Number of climatization equipment purchased between 1 and 5 years ago [units]
0.46
0.61
0.1683
Number of climatization equipment purchased in the past 5–10 years [units]
0.99
0.61
p < 0.01
Number of climatization equipment purchased over the past 10 years [units]
0.49
0.55
0.6248
There were significant differences in average annual household income and average household financial assets at the 1% and 5% levels. Fuel-poor households had lower annual household income and lower household financial assets than other households. While no differences were found in housing, some differences were found in the ages of the climatization equipment. Fuel-poor households possess climatization equipment purchased between 1 and 5 years ago than other households. Conversely, fuel-poor households possess fewer climatization equipment purchased in the past year or less than other households. These results suggest that fuel-poor households own relatively old climatization equipment, whereas other households own relatively new climatization equipment.
Table S3 (see Supplementary Materials) presents the analysis of the impact of the type of climatization equipment ownership and insulation renovation on fuel bills using a correlation matrix. The upper right corner of the diagonal line in the correlation matrix represents the correlation coefficient. The results of the p-value determination by the test of uncorrelatedness of the number of correlation coefficients are also shown, and based on the items for which the p-value was significant at the 1% or 5% level, the readable results are shown below.
The percentage of fuel-poor households increased as the fuel and electricity prices increased. Higher energy prices increase the number of fuel-poor households. No correlation was found between the number of climatization equipment and the fuel or electricity bills. Lowering the temperature settings of climatization equipment in the spring, summer, and fall indicates an increase in fuel bills. Electricity bills were significant only in the summer.
Fuel and electricity bills increased with the implementation of insulation re-modelling. One factor is that the implementation of insulation re-modelling lowers the set temperatures of climatization equipment in spring, summer, fall, and winter. Lowering the temperature setting in winter effectively reduces fuel and electricity bills; however, lowering the temperature setting of climatization equipment in spring, summer, and fall increases fuel and electricity bills. Insulating houses usually involve making a house airtight to create a structure that makes heat breaking out of the house difficult (Energy Star, 2024). Insulation remodelling has been promoted in Japan from the perspective of energy conservation (Japan Ministry of Land, Infrastructure, Transport and Tourism, 2024). However, this structure might make it easier to store heat from sunlight and other external sources inside the house, especially in summer, and the house must rely on air conditioning to cool it. This can result in higher fuel and electricity costs.
A study by Walker et al. (2014) on primarily elderly households noted that fuel-poor households are characterized by living in older homes with poor insulation, using older heating equipment with poor fuel efficiency, having low incomes, and not being able to afford to invest in insulation or energy-efficient heating. This study was conducted in the United Kingdom between 2000 and 2013. The Warm Front program, implemented in the UK from 2000–2013, improved energy efficiency in 2.36 million fuel-poor households by insulating and renovating their homes and replacing heating appliances (Sovacool, 2015). Thus, insulation renovation has an effectivity as a part of the fuel poverty program. However, the results of this study suggest that a measure can increase comfort, but not result in energy savings.
Simulation of fuel poverty measures
A model combining a binomial logistic regression equation and a multiple regression equation was derived to measure the effectiveness of the fuel-poor household measures. First, a binomial logistic regression equation was developed to determine fuel-poor households. Here, the explained variable was set as fuel-poor households in 2022. If the value was 1, the household was determined to be fuel-poor. The household was not determined to be fuel-poor if the value was zero. The explanatory variables were household income in 2022, and fuel bills in 2022, directly related to the 10% and LIHC indicators used to calculate fuel-poor households. Table 5 presents the results of calculating partial regression coefficients from the binomial logistic regression analysis. Nagelkerke’s R2, representing the correlation coefficient, was as high as 0.844. All p-values for each explanatory variable were significant at the 1% level. The explanatory variables suggest that household income is a negative factor for fuel-poor households and that the fuel bill is a positive factor.
Table 5
Partial regression coefficients for binomial logistic regression analysis
Variables
Partial regression coefficient
Odds ratio
p-value
Annual household income (2022) [10,000 JPY]
−0.0733
0.929
p < 0.01
Annual fuel bill (2022) [JPY]
0.0000513
1.000
p < 0.01
Constant
2.565
13.005
p < 0.01
Next, multiple regression equations were used to develop regression equations for estimating the annual fuel bills. The number of household members, total floor space, and climatization equipment temperature settings (summer) were selected as candidates for explanatory variables based on a survey of items highly correlated with annual fuel bills. Using these three items as explanatory variables, Table 6 shows the results of the partial regression coefficients calculated using the multiple regression analysis. R2 was as low as 0.193. However, because all the p-values for each explanatory variable were significant at the 1% level, this regression equation was adopted in this study. The explanatory variables suggest that the temperature setting of the climatization equipment (summer) is a negative factor for fuel bills, while fuel bills are a positive factor.
Table 6
Partial regression coefficients for multiple regression analysis
Variables
Partial regression coefficient
p-value
Total floor area (1–6)*
16,416
p < 0.001
Number of persons in household [persons]
25,744
p < 0.001
Climatization equipment temperature setting (summer) [°C]
−11,822
p < 0.001
Constant
360,106
p < 0.001
*1: 29 m2 or less, 2: 30–49 m2, 3: 50–69 m2, 4: 70–99 m2, 5: 100–149 m2, 6: 150 m2 or more
Combining these two models can calculate the lower limit of fuel bills incurred by fuel-poor households. This section analyses the trends in each of the three patterns: all households, elderly households, and single-mother and single-father households. The initial values of the explanatory variables for each pattern obtained from the questionnaire survey results are listed in Table 7. The following three cases were analyzed: Case 1 (varying the annual household income), Case 2 (varying the temperature setting of climatization equipment (in summer)), and Case 3 (varying the duration of climatization equipment use).
Table 7
Initial values of explanatory variables
Variables
All households
Elderly households
Maternal and paternal households
Annual household income (2022) [10,000 JPY]
601.75
374.75
411.76
Total floor area (1–6)*
3.59
4.14
2.65
Number of household members [persons]
2.40
1.75
3.17
Climatization equipment temperature setting (summer) [°C]
26.04
26.99
26.00
*1: 29 m2 or less, 2: 30–49 m2, 3: 50–69 m2, 4: 70–99 m2, 5: 100–149 m2, 6: 150 m2 or more
Case 1: Varying annual household income
For each pattern, the household income threshold at which a household was considered fuel-poor was identified. In this section, we examine whether and to what extent the household income range is determined to be fuel poverty when household income varies from 0 to 7 million JPY in 500,000 JPY increments. The initial values of other items were fixed. Figure S1 (see Supplementary Materials) shows the results for each pattern. The results show that the condition for exclusion from the determination of fuel-poor households was above 1.5 million JPY in the case of all households and single-parent households. elderly households were conditioned to exceed 1 million JPY.
Case 2: Varying the temperature setting of climatization equipment (in summer)
Fuel-poor households in Case 1 can take appropriate measures. One is to reduce electricity bills by increasing the temperature setting of climatization equipment during the summer. To save energy without compromising comfort, it is recommended that the room temperature be set at 28 °C in summer and 20 °C in winter (Japan Ministry of Environment, 2020). Based on this recommendation, we investigated which settings and below were considered fuel poverty when the climatization equipment temperature setting in summer varies from 20 °C to 35 °C in 1 °C increments. In this case, Household income was fixed at the values derived in Case 1 (150 [10,000 JPY] for all households and single-parent households and 100 [10,000 JPY] for elderly households). The initial values of the other items were fixed.
Figure S2 (see Supplementary Materials) shows the results for each pattern. Consequently, all households can move out of fuel-poor households by setting the climatization equipment at 28 °C during the summer. This was consistent with the Japan Ministry of Environment (2020) recommended temperatures. For single-parent households, the temperature is 29 °C, slightly higher than the recommended temperature mentioned above. However, the temperature should be increased to 33 °C for elderly households. Raising the temperature of climatization equipment significantly in summer might lead to a decrease in electricity bills but might also increase the risk of heat stroke. Considering that the elderly is at particularly high risk of developing heat stroke (Lien & Tabata, 2022), it is not realistic to raise the temperature setting of climatization equipment to 33 °C.
Case 3: Varying the duration of climatization equipment use
The Japan Ministry of Environment (2024a) promotes an activity called "energy sharing" in which people gather to cool off during the hot summer hours in facilities where climatization equipment are in use, such as neighbourhood public facilities and shopping centres. Such activities can reduce the use of climatization equipment in homes during summer, thereby reducing fuel bills. Similarly, heat sharing reduces the time spent using home climatization equipment during winter (Japan Ministry of Environment, 2024b). Given that reducing fuel bills by raising the temperature setting of the climatization equipment is not realistic for elderly households, as in the measures in Case 2, citizens will visit locations where energy sharing can occur and consider measures to reduce the time spent using climatization equipment during the day at home.
Here, household income is fixed at the values derived in Case 1 (1.5 million JPY for all households and single-parent households and 1 million JPY for elderly households). The initial values for the total floor space, number of household members, and climatization equipment temperature settings were fixed, while the fuel bills were varied. The 2021 and 2022 survey results indicate that electricity will account for 56.6% of fuel bills. The share of heating and cooling in the annual electricity consumption in the Kansai region including Kobe City is 19.2% (cooling: 6.3%, heating: 12.9%) (The Institute of Energy Economics, Japan, 2020). Based on this, the fuel bill's share of electricity used for cooling and heating was 34.0%. The percentage reduction in electricity bills due to cooling and warming sharing is then calculated. The average hours of climatization equipment usage per day were based on the results of the Tabata and Tsai (2019). This was multiplied by the number of days by season and weekdays/holidays to estimate the hours of air-conditioning use for a year. It was assumed that energy sharing was performed during the 8 h between 10:00 am and 5:00 pm. By implementing energy sharing, the time required for cooling and heating was reduced, and electricity bills were reduced by 10.3% (cooling: 3.5%, heating: 6.8%). Considering these factors, fuel bills can be reduced by 30.3% for each pattern by implementing energy sharing.
Based on the above results, the model's simulations show that all-households and single-parent households can move out of fuel poverty by implementing energy sharing. However, elderly households cannot escape fuel poverty even after implementing energy sharing. Energy sharing can effectively avoid fuel poverty; however, elderly households require additional measures. If elderly households can move out of fuel poverty, raising their annual household income by 0.2 million JPY would be necessary. This finding suggests a subsidy policy targeting elderly households against energy price hikes is required.
Consider the impact of rising energy prices due to climate change mitigation measures on fuel poverty
Energy prices may fall as the energy supply–demand balance stabilizes. However, Bolton (2010) states, "The era of cheap energy is over." Cheap energy prices cannot be expected to remain as low as they were in the past. Considering that energy prices would remain high, it is necessary to consider measures to relieve households more vulnerable to high energy prices. This also needs to be done carefully regarding how policies can increase energy prices. One example is carbon pricing intended for climate change.
Policies such as carbon pricing increase energy prices and negatively affect more vulnerable households (Kosch et al., 2022; Yu et al., 2024). Dorband et al. (2019) indicated that a carbon price of 30 USD/t-CO2 reduces income by up to 2.5% for low-income households. Reaños (2021) indicated that a 1% increase in carbon taxes would increase the probability of fuel poverty by 0.5%. Japan's feed-in tariff, introduced in 2012, has increased the country's installed renewable energy capacity to a globally high level. However, this has resulted in a considerable burden on households in the form of renewable energy surcharges, amounting to 897 JPY per household per month in FY20223 (Chubu Electric Power Miraiz Co. Inc., 2022). Although the FY2023 renewable energy surcharge was lowered to counteract rising energy prices, this measure was not permanent. Carbon pricing may contribute to achieving the goals of Sustainable Development Goal (SDG) 13, but increasing the number of households with high vulnerability to energy price hikes, such as the socially vulnerable, is undesirable from the perspective of SDG 1 and 3. To build a society where "no one is left behind," the slogan of the SDGs, it is necessary to design policies that can aim to achieve these goals simultaneously.
Conclusions
This study also empirically analyzed the incidence of fuel-poor households in the increase in energy price. This study also proposed countermeasures for households vulnerable to energy price hikes and measures their effectiveness. A questionnaire survey considering the recovery from the COVID-19 pandemic was conducted to determine households’ vulnerability to recent increase in energy price. The 10% and LIHC were used to calculate the change in fuel-poor households. This study also proposes measures to help fuel-poor households move out of fuel poverty and measures their effectiveness. The key findings of this study are as follows.
(1)
The percentage of fuel-poor households in 2022 was 5.39%, an increase of 0.23% over 2021. This suggests that fuel-poor households are increasing in response to increasing energy prices. The 2022 fuel poverty rate for elderly households was 1.48%, and it was 3.91% for households other than the elderly. Both showed an increase from the 2021 results.
(2)
A model combining binomial logistic regression and multiple regression analyses was derived to measure the effectiveness of fuel poverty measures. For households with incomes determined to be fuel-poor to move out of fuel-poor households, measures to reduce electricity bills by increasing the temperature setting of climatization equipment during summer months were studied using the model. As a result, all households and single-parent households were required to set the temperature at 28 °C and 29 °C, respectively, in order to move out the fuel-poor households. For elderly households, the required temperature was 33 °C, which is unrealistic.
(3)
To help households living in fuel poverty and those with incomes that are considered fuel-poor move out of fuel poverty, measures to reduce the amount of time spent using climatization equipment during the day in summer through energy sharing programs was discussed using the model. The study team went to locations where energy sharing was available and aimed to reduce climatization equipment use at home by eight hours per day. As a result, it was found that all households and single-parent households could move out of fuel poverty. However, elderly households were unable to exit, and an additional condition for exiting was an increase in the annual household income of elderly households by 0.2 million JPY.
We determined the impact of recent energy price hikes on households using a fuel poverty index. Using the index, it was also possible to analyse the effectiveness of the measures implemented to relieve households with high vulnerability to increase in energy price. Multi-dimensional analysis using a multi-parameter can be employed to identify fuel poverty households and analyse their characteristics in an accurate manner. However, complex models are difficult to use when national and local governments implement fuel poverty policy evaluations. Models that provide sufficient results with a small number of parameters are needed for use in policy evaluation. In this context, the study expects to have developed a simple model that is easy to use by national and local governments.
Three future prospects for research using the fuel poverty index are described. The first is to observe the relationship between energy price trends and fuel poverty by accumulating time-series data. It is not desirable to have a proportional relationship between the increase in energy prices and fuel poor households. Preferably, these variables should decouple. To examine such policies, it is important to observe the changes over time. The second is to reconsider the 10% indicator commonly used in fuel poverty studies. As Herrero (2017) criticizes, the 10% indicator is an old UK indicator that reflects the socioeconomic characteristics of households. It is necessary to propose conditions for setting thresholds that can reflect the realities of each country and region, considering the realities of each country and region's climate and the socioeconomic characteristics of households, etc. The third is the need for a policy evaluation methodology to mitigate the negative impact on fuel poverty of the implementation of environmental and energy policies that may affect energy prices, such as carbon pricing. This evaluation methodology would include projections of the number of fuel poor households as energy prices change and an analysis of the effectiveness of mitigation measures to reduce the number of fuel poor households. A concise and intuitive policy evaluation methodology would be accessible to national and local governments.
Acknowledgements
This research was supported by the Urban Innovation KOBE, founded by Kobe City of Japan, the Mitsui Sumitomo Insurance Welfare Foundation, and the Sumitomo Foundation.
Declarations
Ethical approval
All procedures used in this research were approved by the Ethical Committee of Kobe University.
Conflict of interest
There are no conflicts of interest to declare.
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