Impact of Regional Characteristics on Energy Consumption and Decarbonization in Residential and Transportation Sectors in Japan’s Hilly and Mountainous Areas

Abstract

In Japan’s hilly and mountainous areas, which cover over 60% of the national land area, issues such as population outflow, aging, and regional decline are intensifying. This study explored sustainable decarbonization pathways by examining two representative regions (Maniwa City and Hidakagawa Town), while accounting for diverse regional characteristics. A bottom-up approach was adopted to calculate energy consumption and CO₂ emissions within residential and transportation sectors. Six future scenarios were developed to evaluate emission trends and countermeasure effectiveness in different regions. The key findings are as follows: (1) in the study areas, complex regional issues have resulted in relatively high current levels of CO₂ emissions in these sectors, and conditions may worsen without intervention; (2) if the current trends continue, per-capita CO₂ emissions in both regions are projected to decrease by only around 40% by 2050 compared to 2020 levels; (3) under enhanced countermeasure scenarios, CO₂ emissions could be reduced by >99%, indicating that regional decarbonization is achievable. This study provides reliable information for designing localized sustainability strategies in small-scale, under-researched areas, while highlighting the need for region-specific countermeasures. Furthermore, the findings contribute to the realization of multiple Sustainable Development Goals (SDGs), particularly goals 7, 11, and 13.

1. Introduction

Countries and regions around the world exhibit considerable differences in terms of geography, climate, culture, resources, economy, population structure, and social life, which contribute to the formation of unique regional characteristics. These differences have led to the emergence of representative regional landscapes, cultivation of rich historical and cultural heritage sites, and establishment of specific lifestyle patterns, thereby creating unique attractions for each region. It is widely recognized on a global scale that energy, environment, and sustainable development are closely related [1]. However, as global issues such as climate change and energy crisis intensify, their adverse impacts on daily life and social development cannot be neglected. Identifying countermeasures to address these problems has become an urgent social issue, drawing attention to initiatives such as SDGs and carbon neutrality. In 2015, the United Nations adopted the 2030 Agenda for Sustainable Development, which includes 17 goals and 169 targets [2]. While the SDGs provide a universal framework, the path to achieving these goals varies significantly across countries and regions due to differences in regional characteristics. Meanwhile, diverse regional characteristics profoundly influence regional energy supply and demand, greenhouse gas (GHG) emissions, and decarbonization efforts. For example, regarding the impact of climate, in cold regions, prolonged heating usage is a major factor contributing to energy consumption, resulting in a tendency for higher per-capita energy consumption during winter, whereas in tropical regions, the demand for cooling energy is more pronounced. Furthermore, factors such as the regional economy, urbanization degree, population distribution and structure, and lifestyle patterns are closely related to the local energy consumption characteristics. In Japan, the center of development has been concentrated in urban areas during the high-growth process of the economy, and urbanization has further widened the differences between regions. While dense cities benefit from efficient infrastructure and public transport, they face high electricity demand due to business concentration [3]. Conversely, rural areas struggle with aging infrastructure and inefficient energy use [4]. In Japan, where mountainous topography is widespread, hilly and mountainous areas occupy more than 60% of the country’s total land area and are responsible for producing nearly 40% of its agricultural output [5]. Nonetheless, these important regions are facing acute challenges related to depopulation and aging, largely due to large-scale population outflow to the urban areas. At present, only around 10% of the population continues to live in these areas. As current challenges are expected to persist, concerns are mounting over their long-term sustainability and future development. Accordingly, region-specific strategies for achieving the SDGs in such regions merit further exploration.

Furthermore, Japan has set a target of cutting GHG emissions by 46% from 2013 levels by 2030, along with a long-term goal of reaching carbon neutrality by 2050 [6]. Attaining these ambitious targets will require unprecedented measures that deviate from past trends in both energy supply and demand. Since carbon dioxide (CO₂) is estimated to represent around 75% of total global GHG emissions [7], this study focuses specifically on CO₂ emissions. While urban areas have received greater attention, it remains essential to include hilly and mountainous areas in the national carbon neutrality framework, given their substantial geographic coverage. Achieving comprehensive decarbonization requires that no region be overlooked.

Overall, Japan’s hilly and mountainous areas exhibit significant variations in terms of resources, land use, population structure, and living conditions. These diverse regional characteristics have considerable impacts on energy consumption and CO₂ emissions. As regional issues such as depopulation and aging become increasingly severe, achieving carbon neutrality is expected to have broad positive benefits, such as mitigating economic decline, promoting the adoption of new technologies, enhancing environmental quality, strengthening energy security, and advancing regional sustainable development. In this regard, the SDGs—particularly SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action)—provide an important global framework [2]. Therefore, it is essential to comprehensively consider the challenges of depopulation and aging alongside energy and environmental concerns and adopt region-specific strategies to achieve the SDGs tailored to the unique characteristics of each area.

More specifically, this study aims to address the following research questions:

(1)

What are the future development trends of Japan’s hilly and mountainous areas?

(2)

What are the regional characteristics and challenges faced by these areas, and how do they affect regional energy consumption and CO₂ emissions?

(3)

How do energy consumption and CO₂ emission patterns in hilly and mountainous areas differ from those in urban areas? Are there notable similarities or differences among various hilly and mountainous areas?

(4)

Under the current development trajectory, is the decarbonization of the residential and transportation sectors in these regions achievable by 2050?

(5)

What measures are necessary to achieve regional decarbonization, and how do regional characteristics influence the effectiveness of such measures?

The objective of this study is to explore sustainable decarbonization pathways for Japan’s hilly and mountainous regions, which face distinct demographic, infrastructural, and geographical challenges. By integrating and analyzing diverse regional characteristics (climate conditions, resource availability, population structure, land use patterns, etc.), this study examines the characteristics of regional energy consumption and CO₂ emissions within the residential and transportation sectors. Through scenario-based simulations of various decarbonization strategies, the study aims to assess the feasibility of achieving carbon neutrality by 2050. The findings aim to provide region-specific evidence data to support effective policy development for small-scale, under-researched areas.

2. Literature Review

2.1. Studies on the Impact of Regional Characteristics on Energy Consumption and CO₂ Emissions

According to the Statistical Review of World Energy [8], which describes the regional energy conditions in 2023, significant differences exist among regions in terms of energy consumption volume, energy consumption structure, and renewable energy supply. These differences can be attributed to a wide range of regional characteristics, including natural environmental factors (climate conditions, topography, resources, etc.), social and economic factors (population structure, degree of urbanization, economic activity, etc.), and cultural and lifestyle factors (housing types, transportation modes, energy usage habits, etc.). Numerous studies have investigated this topic. Research on the impact of regional characteristics on energy consumption and CO₂ emissions has focused on various aspects such as climate [9,10], resources [11,12], urbanization and economics [13,14], population distribution and structure [15,16], and housing types and lifestyles [17,18]. Studies focusing on climate as a key determinant [9] have highlighted the complexity of climate change effects. For example, as climate change and global warming progress, energy consumption for cooling residential and commercial buildings has increased significantly, which in turn may further exacerbate climate change. It is noteworthy that CO₂ emissions from human activities are considered the primary driver of climate change. The study by Fawzy et al. [10] also indicated that maintaining current emission levels could further accelerate global warming and increase the likelihood of various natural disasters in the future. Climate change has already threatened key sectors such as food, water, ecosystems, and human habitats. Meanwhile, global societal development continues to drive a sharp rise in energy demand, which remains largely dependent on fossil fuels, whose combustion releases significant amounts of GHG that exacerbate the threats to human life and health [11]. Transitioning to renewable energy is widely recognized as one of the most effective strategies to reduce the reliance on fossil fuels, significantly lowering CO₂ emissions and enhancing environmental quality. However, due to differences in resources, economic conditions, and policies, there are substantial disparities among countries and regions in the adoption rate of renewable energy and technological advancements [12]. Considering the many complex issues that still exist, the further expansion of renewable energy adoption should be planned in accordance with local conditions and regional characteristics. On the other hand, a study focusing on demographic factors [15] suggested that significant changes in population size, age composition, and distribution are expected in many regions in the future. Particularly, senior citizens have played a leading role in driving up CO₂ emissions in recent years, thereby exacerbating the challenges of carbon mitigation in aging societies [16]. Therefore, regional issues such as depopulation and aging should not be overlooked because they not only influence CO₂ emissions but also pose serious threats to regional sustainability.

In conclusion, these studies have demonstrated the impact of regional characteristics on energy consumption and CO₂ emissions, emphasizing the importance of considering multiple factors comprehensively when conducting research in different regions. Regarding studies that incorporate a comprehensive view of regional characteristics, previous studies have focused on countries such as China [19], the United States [20], Greece [21], and Japan [3,22,23], highlighting the intranational variations at city and regional levels. A study in the United States [20] analyzed the relationship between climate, population structure, land use, industrial structure, and CO₂ emissions, revealing significant differences between urban and rural areas. A study in Japan [3] classified municipalities based on their CO₂ emission characteristics and examined the relationship between regional characteristics and CO₂ emission patterns. The results of this study reveal that different sectors are predominantly influenced by varying factors; for instance, climatic conditions have a greater impact on the residential sector, whereas economic factors exert a stronger influence on the commercial sector. Overall, the residential sector is most strongly influenced by regional characteristics, with notable impacts on other sectors. Moreover, in 2022, it was reported that 21.6% of Japan’s total GHG emissions originated from two sources directly related to everyday life: residential energy use and private vehicle transportation [24]. Based on the above analysis, the implementation of targeted decarbonization measures for these sectors is essential.

2.2. Studies on the CO₂ Emissions Reduction and Carbon Neutrality in Small-Scale Regions

A considerable number of studies worldwide have assessed the energy usage and CO₂ emissions within the residential and transportation sectors, particularly focusing on developed countries and large-scale geographic areas such as the United States [25], China [26], Europe [27], and Japan [28]. While research targeting large-scale regions is useful for grasping the overall energy demands and renewable resource potential at national or international levels, concerns persist regarding the precision of foundational data and the relevance of such findings when applied to small-scale regions characterized by distinct local conditions. Small-scale regions possess unique local characteristics that significantly influence both regional energy consumption and the supply of renewable energy. For example, in Japan’s hilly and mountainous areas, the percentage of CO₂ emissions from the residential and transportation sectors are projected to be relatively higher. This is largely attributed to the dominance of older housing stock and a stronger reliance on private vehicles for daily commuting needs [4,29]. Furthermore, rural areas hold considerable decarbonization potential due to their abundant land and natural resources, with studies [30,31] indicating that renewable energy technologies like biomass, solar, and wind could theoretically enable carbon neutrality in these rural communities. However, on a global scale, small-scale regions are facing serious challenges such as population decline and regional deterioration, posing significant threats to their future viability [32]. For these regions, pursuing decarbonization is an effective means to enhance regional stability and sustainability. Nevertheless, the lack of relevant studies and data may lead to misinterpretation of the current conditions and development potential, resulting in misguided policy interventions and delays in advancing the decarbonization process. Although previous studies have confirmed the potential for significant regional CO₂ reduction, considerable disparities remain in the implementation of decarbonization measures across regions, with larger regions generally achieving higher implementation rates than smaller ones [33,34]. This may be due to worsening financial conditions, a shortage of personnel dedicated to climate change countermeasures, or differing priorities in addressing regional issues in smaller areas. Because hilly and mountainous areas face challenges associated with population decline and an increasing need for decarbonization, it is essential to provide reliable information tailored to local conditions. This requires the accurate assessment of regional energy demand, renewable energy supply potential, and quantitative evaluation of the effectiveness of various decarbonization measures.

2.3. Progress and Localization of the SDGs

SDGs represent a global action framework designed to address increasingly complex and interlinked social, economic, and environmental challenges, with the overarching aim of promoting balanced and sustainable development for the future of all humanity. Since their adoption in 2015, the SDGs have attracted significant academic and policy attention, with expanding research on their historical evolution [35], the development of evaluation frameworks [36,37], and the assessment of the progress situation [38,39]. A recent study [40] analyzed over 12,000 related publications between 2015 and 2022 to evaluate the present state of progress, as well as the challenges and opportunities surrounding the SDGs. The findings revealed notable regional differences in research priorities concerning the various goals and targets of the SDGs. For instance, studies in developed countries tend to focus on SDG 4 (Quality Education), SDG 11 (Sustainable Cities and Communities), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). In contrast, research in African regions shows a stronger emphasis on SDG 1 (No Poverty), SDG 2 (Zero Hunger), SDG 5 (Gender Equality), SDG 6 (Clean Water and Sanitation), SDG 13 (Climate Action), and SDG 15 (Life on Land). These variations reflect the inherent regional differences in socioeconomic conditions, natural resources, and development stages, highlighting the importance of localization and prioritization in SDG implementation strategies [41,42]. Accordingly, more place-based, region-specific research is essential to support evidence-based policymaking that advances the process of achieving global SDGs.

2.4. Research History and Contribution of This Study

Regarding the research focused on Japan’s hilly and mountainous areas, our group previously conducted studies in Hidakagawa Town, Wakayama Prefecture [43,44,45,46,47], and Maniwa City, Okayama Prefecture [48,49]. Previous studies have included surveys on current living conditions [43], daily traveling environments [44,49], evaluations of the quality of living environments [45,46], and quantitative analyses of decarbonization scenarios [47,48,49]. Particularly, as a foundational study for the present research, the previous work focusing on Hidakagawa Town provided a detailed description of the methodological framework, calculation procedures, and relevant data sources [47]. The scenario-based simulation results demonstrated the potential for CO₂ emission reduction and the achievement of carbon neutrality in hilly and mountainous areas. However, all these previous studies were conducted independently for each region, and further investigation is required to evaluate the accuracy of the research methods and findings, and the effectiveness of decarbonization measures when applied across different regions. Based on the above, the originality and key contributions of this study can be summarized as follows:

(1)

Given the current lack of research focusing on small-scale regions on the global scale, this study emphasizes Japan’s hilly and mountainous areas, providing targeted insights through quantitative analysis and future scenario simulations to support local governments in formulating decarbonization and sustainable development strategies aligned with the SDGs, particularly goals 7, 11, and 13.

(2)

The study fully considers the realistic challenges specific to these regions, such as depopulation, aging, and dispersed settlements, incorporating these factors into CO₂ emission estimations and decarbonization scenario modeling to more accurately reflect the barriers faced in local decarbonization efforts.

(3)

Building upon earlier works that analyzed Hidakagawa Town and Maniwa City separately, this study is the first to conduct an integrated, comparative analysis. By conducting comparative analysis from multiple perspectives, the study not only validates the accuracy of the proposed estimation methods and findings but also highlights how different regional characteristics affect energy consumption and decarbonization potential, thereby offering practical evidence to guide the localization of SDG implementation.

3. Study Areas

3.1. Maniwa City, Okayama Prefecture

Study area 1 was Maniwa City, Okayama Prefecture [50], in the Chugoku region of Japan (Figure 1). Maniwa City, located in the northern part of Okayama Prefecture and almost in the center of the Chugoku Mountains, was formed by the merger of nine towns and villages (i.e., Hokubo, Ochiai, Kuse, Katsuyama, Mikamo, Yubara, Chuka, Yatsuka, and Kawakami). It is the largest city in the prefecture, with a total area of 828 km²; however, its inhabitable land area is 167 km², which is only 20% of the total area, and the remaining 80% of the land is mountainous and forested. As of 2020, the population was 42,923 with 17,647 households and 139 settlements in the area. The overall aging rate (i.e., the percentage of population aged 65 and over) in Maniwa City in 2020 was 40%. By town and village, the lowest aging rate was 33% in Kuse (i.e., the urban center), and the highest rates were in the mountainous areas of Mikamo and Yubara (55% and 51%, respectively).

3.2. Hidakagawa Town, Wakayama Prefecture

Study area 2 was Hidakagawa Town in Wakayama Prefecture [51] in the Kinki region of Japan (Figure 1). Hidakagawa Town, located in the Hidaka area of Wakayama Prefecture, was formed by the merger of three towns and villages (i.e., Kawabe, Nakatsu, and Miyama). It has a total area of 332 km², making it the third largest municipality in this prefecture by land area. However, only 42 km² (13% of the total area) is designated as habitable. As a representative example of Japan’s hilly and mountainous area, forest covers approximately 87% of the landscape, marking it as the dominant land use. As of 2020, the town had a population of 9139 residing in 3739 households across 86 settlements. Aging rates in 2020 showed a distinct upward trend from urban to mountainous areas, with Kawabe (i.e., urban area) at 28%, Nakatsu (i.e., hilly area) at 39%, and Miyama (i.e., mountainous area) at 47%.

3.3. Analysis of the Regional Characteristics

3.3.1. Population Structure and Distribution

As of 2020, the population of Maniwa City was approximately 4.7 times higher than that of Hidakagawa Town. Analyses of the population composition showed that young, working-age, and elderly populations in Maniwa City accounted for 11%, 49%, and 40%, respectively, whereas in Hidakagawa Town, these figures were 11%, 53%, and 36%, respectively (Figure 2a). Both regions were experiencing severe population aging; however, this issue appeared more pronounced in Maniwa City. Furthermore, women of childbearing age accounted for a very low proportion in both regions, which is expected to lead to a future decline in birth rates. Regarding household composition, only elderly households (including aged single households and aged couple households) constituted a significant proportion, reaching 37% in Maniwa City and 40% in Hidakagawa Town (Figure 2b).

Additionally, the two study areas exhibited clear differences in their population distributions. The habitable land population densities in Maniwa City and Hidakagawa Town were 257 and 218 persons/km², respectively. Depopulation tended to be more severe in Hidakagawa Town, which has a larger proportion of mountain forests. Maniwa City extends from north to south, with its center located in Kuse, near the middle of the city. The four districts of Ochiai, Kuse, Katsuyama, and Hokubo, located in the lower-altitude southern and central areas, accounted for approximately 81% of the total population of the city. Additionally, the Hiruzen Plateau in the northern part of the city serves as a well-known agricultural and tourist destination, resulting in a certain degree of population and facility concentration. In contrast, Hidakagawa Town has an east–west geographical distribution, with an even more pronounced disparity in population and facility distribution. The town center is in Kawabe, which borders Gobo City in the west. As one moves eastward into the mountainous regions, the residential population and distribution of facilities become increasingly sparse.

3.3.2. Climate

In this study, standardized year-extended Amedas meteorological data [52] were used to compare the climate conditions of the two regions throughout the year. The annual maximum temperature was 35.4 °C in Maniwa City and 36.4 °C in Hidakagawa Town. Although the annual maximum temperature in Maniwa City was slightly lower than that in Hidakagawa Town, the number of hours exceeding 30 °C was 91 h longer in Maniwa City. Regarding the annual minimum temperature, Maniwa City recorded −4.3 °C and Hidakagawa Town recorded −2.0 °C. The two regions exhibited significant temperature differences, particularly during winter. In Maniwa City, the number of hours below 2 °C reached 1124 h annually, which was 5.1 times higher than that of Hidakagawa Town. Figure 3 shows the monthly average, maximum, and minimum temperatures in both regions, and indicates that the temperature variation throughout the month was larger in Maniwa City. Overall, Maniwa City was colder than Hidakagawa Town and experienced greater temperature fluctuations. These climatic characteristics can influence household energy consumption and CO₂ emissions, particularly regarding air conditioners (ACs), water heaters, and other appliances.

3.3.3. Existing Residences

As hilly and mountainous areas rich in forest and timber resources, both Maniwa City and Hidakagawa Town have a high proportion of wooden houses, accounting for 96% and 92%, respectively. Therefore, this study focused exclusively on detached wooden houses. Regarding insulation performance, approximately 50% of existing houses in both regions were built before 1980, i.e., before the old energy-efficiency standard was established. Overall, the existing residences in both regions are characterized by a high proportion of wooden buildings with low insulation performance. Regarding photovoltaic (PV) systems installed on residential rooftops, according to government data [53], the number of PV installations in Maniwa City in 2020 was 1410, accounting for 8.0% of the total number of households that year. There were 306 installations in Hidakagawa Town, accounting for 8.2% of the total number of households. Over the 5-year period from 2015 to 2020, the growth rates of residential PV adoption in Maniwa City and Hidakagawa Town were 1.3% and 2.3%, respectively, indicating more rapid expansion of PV adoption in Hidakagawa Town. Factors such as winter snowfall and insufficient solar radiation in mountainous areas may have hindered the adoption of residential PV systems in Maniwa City.

3.3.4. Daily Mobility

In rural areas, inadequate public transportation is a common issue, leading to declining public transport utilization rates and a high dependence on private vehicles. According to the results of the daily mobility and person trip (PT) surveys [44,49] conducted among residents of both study areas, private vehicles accounted for >90% of daily mobility, indicating minimal use of public transportation as a common daily reality. Regarding private vehicle ownership and usage, the survey results indicated that, in Maniwa City, the average number of vehicles per household was 2.62, with an annual driving distance of 32,576 km (

Table 1. The average number of vehicles and annual driving distances per household.

	Average Number of Vehicles Per Household	Average Annual Driving Distances Per Household [km]
Maniwa City	2.62	32,576
Hidakagawa Town	2.15	29,755
National average	1.30	13,748

). In Hidakagawa Town, the average number of vehicles per household was 2.15, with an annual driving distance of 29,755 km (Table 1). Compared to the national averages of 1.3 vehicles and 13,748 km per household [54], both regions exhibited significantly higher values. Particularly, the annual driving distance per household in these rural areas was over 2.2 times higher than the national average, suggesting that CO₂ emissions from private vehicles were at a high level. A more detailed regional analysis revealed notable differences in daily driving distances per vehicle. In different districts of Maniwa City, driving distances ranged from 33.9 to 51.1 km, with a city-wide average of 39.6 km (

Table A1. Average daily driving distances per vehicle for nine administrative districts in Maniwa City [km].

Administrative Districts	Workday		Holiday		District Average
	Middle Age	Senior	Middle Age	Senior
Hokubo	44.1	46.8	46.7	34.9	44.6
Ochiai	30.4	19.2	51.1	24.0	33.9
Kuse	34.4	17.2	54.1	36.4	37.4
Katsuyama	31.9	40.5	49.0	48.3	38.1
Mikamo	37.4	33.3	47.9	41.2	39.0
Yubara	41.1	29.4	43.2	24.9	39.2
Chuka	39.6	26.1	41.1	22.1	37.7
Yatsuka	48.6	15.4	63.4	59.2	51.1
Kawakami	39.1	37.5	63.6	23.2	42.6

). In Hidakagawa Town, these distances ranged from 25.6 to 39.0 km, with a town-wide average of 32.9 km (

Table A2. Average daily driving distances per vehicle for nine administrative districts in Hidakagawa Town [km] [47].

Administrative Districts	Workday		Holiday		District Average
	Middle Age	Senior	Middle Age	Senior
Yata	31.9	22.5	40.2	29.5	30.1
Hayaso	38.3	31.4	46.7	33.6	38.6
Nyuu	30.7	28.7	34.7	24.9	30.7
Funatsu	37.1	32.4	42.1	36.9	36.9
Kosoura	51.0	26.9	40.3	31.4	39.0
Oboshi	32.9	16.7	42.3	20.7	26.9
Kawakami	37.4	23.6	46.3	29.4	33.0
Aitoku	48.8	36.5	48.8	24.2	38.3
Sogawa	41.4	13.7	39.7	34.8	25.6

), which was significantly lower than that of Maniwa City. One possible reason for this difference is that Maniwa City has a larger geographic area, necessitating longer daily travel distances for commuting and shopping.

Regarding the adoption of next-generation vehicles, as of 2020, the penetration rates of hybrid vehicles (HVs) and electric vehicles (EVs) in Maniwa City were 15.89% and 0.85%, respectively [49,55], and those in Hidakagawa Town were 15.95% and 0.45% [44,55]. In particular, the EV adoption rate remained extremely low.

3.3.5. Regional Decarbonization

Although Japan has declared its goal of achieving carbon neutrality by 2050, significant municipal-level differences remain in target setting, policy formulation, and the implementation of measures. Maniwa City is recognized as a pioneering region in decarbonization. Regarding the introduction of renewable energy, the city has actively promoted biomass power generation as a central initiative [56]. In March 2020, Maniwa City declared its commitment to becoming a “Zero Carbon City Maniwa.” In April 2022, the city was selected as among the “First Decarbonization Leading Areas” in Japan. In the future, Maniwa City plans to establish a second biomass power plant to advance its goal of achieving zero carbon emissions by 2050. However, Hidakagawa Town has been relatively slow in its decarbonization efforts. In November 2021, Hidakagawa Town declared its goal to achieve net-zero CO₂ emissions by 2050 [57]. The plan to achieve this goal includes measures such as building decarbonization, promoting energy-saving behavior, expanding solar power generation, and promoting fuel-efficient vehicles [58].

By 2024, specific measures and related subsidy programs will be established in both areas. In Maniwa City, subsidies include financial support for PV system installation (≤JPY 150,000), high-efficiency water heaters (≤JPY 50,000), next-generation vehicles (≤JPY 300,000), and new houses constructed with locally sourced timber (≤JPY 900,000) [59]. In Hidakagawa Town, subsidy programs include support for PV system installation (≤JPY 100,000), storage batteries (≤JPY 200,000), LED home lighting (≤JPY 100,000), home renovation (≤JPY 100,000), and use of locally sourced timber (≤JPY 200,000) [60].

4. Methodology

4.1. Research Methods for the Residential Sector

This research employed a bottom-up methodology to assess the energy usage and CO₂ emissions within the residential and transportation sectors. The procedural framework is shown in Figure 4.

For the residential sector, representative housing models were developed with detailed settings for building insulation performance, home appliance specifications, lifestyle behavior, and outdoor temperature data according to the current situation in each area. The primary data source was the Total Residential End-use Energy Simulation (TREES) model [28], created by Osaka University’s Shimoda Laboratory, which provides energy consumption estimates based on household composition and behavior. For consumption categories less affected by weather conditions, such as kitchen gas use, data were used directly. However, since the TREES database does not incorporate meteorological data specific to the study areas, certain calculations were conducted separately. For home appliances, lighting, AC, and PV, occupancy patterns, appliance operation schedules, and weather data were reorganized, and separate simulations were carried out using the building energy modeling software, DesignBuilder v6.1 [61]. Thereafter, simulation calculations were performed, and >1000 patterns of residential energy consumption and CO₂ emission data were calculated for each area, depending on different settings in housing type, household type, insulation performance, heat source, and water heater type. These data were then linked to the actual population and household composition data for both regions to estimate the energy consumption and CO₂ emissions in the residential sector for the entire region. Other details of model specifications, regional setup conditions, simulation calculation results, and analysis, are discussed in previous studies [46,47,48].

A portion of the residential simulation results were extracted for comparative analysis. Figure 5 illustrates the annual secondary energy consumption and CO₂ emissions by usage category, assuming a three-person household (i.e., a young couple with one child) living in the same housing model in both regions. The results indicated that all housing models were influenced by climatic conditions, with projected energy consumption and CO₂ emissions being higher in Maniwa City. By usage category, the cooling energy consumption in both regions was similar; however, heating energy consumption showed a significant difference. In the same housing model, the projected heating energy consumption in Maniwa City was 1.4–1.8 times higher than that in Hidakagawa Town, with the gap narrowing as the insulation performance improved. Additionally, in the colder Maniwa City, energy consumption for water heating was slightly higher than that in Hidakagawa Town. These results aligned with the climatic characteristics of both regions. Housing models with lower insulation and airtightness levels were more affected by the climatic conditions. For example, in non-insulated rural-type houses, the heating energy consumption in Maniwa City and Hidakagawa Town was 19.7 GJ and 10.9 GJ, respectively, with an annual CO₂ emission difference of 1.2 t-CO₂. Moreover, across both regions, improved insulation performance corresponded to a gradual reduction in energy consumption. In net zero energy house (ZEH) models with high construction performance and PV systems, the annual power generation was slightly higher in Maniwa City than in Hidakagawa Town. However, when considering the overall supply–demand balance, the lower energy demand in Hidakagawa Town resulted in lower total annual CO₂ emissions in the housing model.

4.2. Research Methods for the Transportation Sector

This study focused on analyzing the private vehicle usage in the transportation sector, with particular attention to the introduction of HVs and EVs (Figure 4). Estimates of energy consumption and CO₂ emissions were based on data from daily mobility and PT surveys conducted in both regions [44,49]. The calculations were based on three key factors: total private vehicle ownership, average driving distance for private vehicles, and the CO₂ emission factors. For Factor 1 (total private vehicle ownership),

Table 2. The average number of private vehicles per household in two study areas.

Number of Household Members	Average Number of Private Vehicles Per Household
	Maniwa City	Hidakagawa Town
1	1.03	1.00
2	2.03	1.87
3	2.52	2.42
4	2.91	2.85
5	3.22	3.49
6	4.15	4.07

presents the average number of private vehicles owned in the two study areas according to the number of household members. The two regions generally showed the same trend in the number of private vehicles owned as the number of people in the household changed. By multiplying the average ownership rates by the projected household composition data, the private car ownership in the two regions was estimated through 2050. For Factor 2 (average driving distance for private vehicles), because the PT surveys provided more detailed information, the simulations also used these distance data, which were distinguished by administrative districts, weekdays versus weekends, and driver types (Table A1 and Table A2). For Factor 3 (CO₂ emission factors), the values were additionally calculated separately for each vehicle type.

For gasoline vehicles and HVs, the CO₂ emissions per vehicle were calculated using Equation (1) [47]:

$$E_{g or h}=\sum _{n=1}^{365}D\times C_{g or h}$$

(1)

• $E_{g or h}$: Annual CO₂ emissions per gasoline vehicle or HV [kg-CO₂];
• $D$: Daily driving distance per vehicle [km];
• $C_{g or h}$: CO₂ emission factors for gasoline vehicles or HVs [kg-CO₂/km].

For EVs, the CO₂ emissions per vehicle were calculated using Equation (2) [47]:

$$E_e=\sum _{n=1}^{365}D\times C_e$$

(2)

• $E_e$: Annual CO₂ emissions per EV [kg-CO₂];
• $D$: Daily driving distance per vehicle [km];
• $C_e$: CO₂ emission factors for EVs [kg-CO₂/kWh].

Finally, the total annual CO₂ emissions in each area were calculated adopting the equation as follows [47]:

$$E={\sum }_{i=1}^{N_{gorh}}{E}_{gorh}+{\sum }_{i=1}^{N_e}{E}_e$$

(3)

• $E$: Regional total annual CO₂ emissions for private vehicles in each study area [kg-CO₂];
• $N_{gorh}$: Number of gasoline vehicles or HVs;
• $N_e$: Number of EVs.

Other detailed study methodology, setup conditions, and calculation formulae can be found in the literature [47].

4.3. Construction of the Decarbonization Scenarios

The analysis in Section 3 revealed that, although the residents of Maniwa City and Hidakagawa Town share certain commonalities in the use of housing and private vehicles, they also exhibit distinct regional characteristics. These regional characteristics have a significant influence on the regional energy consumption and CO₂ emissions. To assess their impact on residential and transportation sector decarbonization, comparable scenarios were developed for both study areas, with projections made every 5 years until 2050. This study incorporated two main approaches for building sustainable decarbonized regions: the introduction of decarbonization measures, and regional redesign. To explore these strategies, multiple scenarios were created, focusing on aspects such as technological progress, population distribution, and urban structure. A summary of these decarbonization measures implemented in each scenario is provided in

Table 3. Decarbonization measures in each scenario.

Scenario	CO2 Emission Factor	Area Aggregation	Residential Sector				Transportation Sector
			Rebuild	Existing Residence		Newly Built House	Introduction of HV·EV	Improve Penetration Rate of HV·EV
				Retrofitting	PV System	ZEH
I	×	×	○	×	×	×	×	×
II	×	○	○	×	×	×	×	×
III	○	×	○	×	○	○	○	×
IV	○	○	○	×	○	○	○	×
V	○	×	○	○	○	○	○	○
VI	○	○	○	○	○	○	○	○

, where “○” indicates implementation and “×” denotes exclusion. The subsequent sections provide a detailed overview of each scenario.

I. Non-countermeasure Scenario: Serving as the baseline scenario, this scenario was designed to assess the impact of various energy-saving measures by assuming that no additional decarbonization measures are implemented. It assumed that residents would remain in their current locations, with the population continuing to decline due to both natural and social factors. Administrative districts would persist as long as at least one household remains. The CO₂ emission factors of the grid and gas were kept constant throughout the whole period [62,63]. Additionally, existing urban residences were rebuilt based on the housing residual rate, and new constructions followed an urban-type design. In the transportation sector, it was set that all private vehicles would remain gasoline-powered, with no introduction of HVs or EVs until 2050.

II. Area Aggregation Scenario: This scenario investigated how redistributing the population influences regional energy use and CO₂ emissions. Migration thresholds were set for each area depending on the local population size. In Maniwa City, districts with <100 households were assumed to be relocated within 5 years to districts with >300 households. In Hidakagawa Town, districts with <30 households were assumed to be relocated within 5 years to districts with >150 households. The number of incoming households was determined based on the target area’s population distribution. It was assumed that all new residences in destination areas would be newly constructed. Additionally, all private vehicles were assumed to remain gasoline-powered, and CO₂ emission factors were constant.

III. Current Trend Scenario: This scenario assumed that progress in the introduction of various measures will remain at current levels for future estimations. First, since the base year of 2013, the CO₂ emission factors of the grid have reduced annually, owing to the continuous implementation of measures such as the expansion of non-fossil energy utilization and improvements in power generation efficiency. Compared to the 2013 level, a 22% reduction was achieved by 2020 [62]. For future projections, based on the current trends and government’s long-term targets, the emission factors of the grid for 2030 and 2050 were set at 0.37 and 0.12 kg-CO₂/kWh, respectively, with values for the intermediate years estimated accordingly (

Table 4. Future setting of CO₂ emission factors of grid and gas.

Scenario	CO2 Emission Factors	2020	2025	2030	2035	2040	2045	2050
I and II	Grid [kg-CO2/kWh]	0.44
	Gas [t-CO2/GJ]	0.059
III and IV	Grid [kg-CO2/kWh]	0.44	0.40	0.37	0.28	0.22	0.16	0.12
	Gas [t-CO2/GJ]	0.059
V and VI	Grid [kg-CO2/kWh]	0.44	0.40	0.37	0.28	0.19	0.09	0
	Gas [t-CO2/GJ]	0.059	0.059	0.053	0.047	0.038	0.026	0.005

). In addition, the penetration rate of PV systems in existing residences, the adoption rate of ZEH in newly built houses, and the penetration rates of HVs and EVs were projected to rise progressively toward 2050, based on 5-year incremental rates observed between 2015 and 2020. In 2020, the PV penetration rate in Maniwa City was 8.0% [53], which is expected to increase to 15.9% by 2050. In Hidakagawa Town, the PV penetration rate was 8.2% [53] in 2020, and is expected to increase to 22.0% by 2050. In 2020, the adoption rate of ZEH was 12.0% [64], which is anticipated to reach 52.2% by 2050. In Maniwa City, the 2020 penetration rates of HVs and EVs were 15.89% and 0.85%, respectively [49], projected to rise to 58.00% and 2.35% by 2050 [55]. In Hidakagawa Town, the 2020 penetration rates of HVs and EVs were 15.95% and 0.45%, respectively [44], projected to rise to 58.00% and 1.95% by 2050 [55].

IV. Area Aggregation + Current Trend Scenario: This scenario integrated the assumptions of Scenarios II and III, to evaluate their combined effect on regional decarbonization.

V. Decarbonization Scenario: This scenario assumed the enhanced introduction of various measures to achieve decarbonization. The CO₂ emission factors of both grid and gas were assumed to decline more significantly than in the current trend scenario (Table 4). In addition, energy-efficient retrofitting was introduced for existing homes, with retrofits applied when urban residences reached 65 years of age and rural residences 80 years. These retrofits included maintaining the basic housing design while enhancing them to meet the 2016 insulation performance standards, converting water heaters to heat pump models, and updating AC to versions with a coefficient of performance corresponding to the target year. Regarding the adoption of PV, ZEH, HVs, and EVs, further expansion of their introduction was assumed. The PV system penetration rate in existing residences was projected to grow by 10% every 5 years, ultimately reaching 70% by 2050. In accordance with the government’s target for improving residential energy-efficiency standards, the ZEH standard was set to be 100% implemented in newly built houses by 2030 [65]. For the transportation sector, the penetration rates of HVs and EVs were aligned with Japan’s next-generation vehicle deployment targets [66], with HVs reaching 69% and EVs 30% by 2030, followed by a full transition to 100% EVs by 2050.

VI. Area Aggregation + Decarbonization Scenario: This scenario integrated the assumptions of Scenarios II and V, to evaluate their combined effect on regional decarbonization.

5. Results

5.1. Future Population and Household Projection Results in the Study Areas

In this study, the cohort-component method was used to estimate population and household structures in each settlement until 2050 based on detailed demographic data in 2020. Death, migration, birth, and marriage rates used for projections in the two regions were obtained from the same source [67].

Table 5. Future population and household numbers in Scenario I.

Area	Item	2020	2025	2030	2035	2040	2045	2050
Maniwa City	Population	42,923	38,669	34,895	31,525	28,190	25,169	22,288
	Household	17,647	16,130	14,721	13,424	11,958	10,498	9123
Hidakagawa Town	Population	9139	8214	7354	6485	5632	4831	4125
	Household	3739	3407	3101	2803	2511	2188	1894

presents the projected population and household numbers for Maniwa City and Hidakagawa Town from 2020 to 2050.

5.2. Estimated Results for Different Sectors

To verify and contrast the estimation results for both the residential and transportation sectors, the 2020 outcomes under Scenario I for the two study areas were extracted and compared with the national statistical data. Figure 6a presents the projected results for the residential sector in both regions along with the national residential sector CO₂ emissions survey conducted by the Japanese government [29]. Estimated annual CO₂ emissions per household were 3.96 t-CO₂ in Maniwa City and 3.39 t-CO₂ in Hidakagawa Town, i.e., both exceeded the national average of 2.88 t-CO₂. Specifically, estimated values in the two areas were 1.4 and 1.2 times higher, respectively, than the national averages. According to a statistical survey [29], average annual household CO₂ emissions in municipalities with populations of <50,000 were 3.33 t-CO₂, indicating a significantly higher level than the total average. The results of the present study largely align with this trend. Influencing factors may include the aging housing stock in rural areas, delays in upgrading household appliances, and increased per capita living space due to population decline. Furthermore, regarding the regional variations in annual emissions, the values for the Kinki region (where Hidakagawa Town is located) were lower than those for the Chugoku region (where Maniwa City is located) [29], which is consistent with the projections of this study. One key factor is that the lower temperatures in Maniwa City may lead to higher energy consumption for heating and water heating.

For the transportation sector in the two study areas, Figure 6b presents a comparison between the 2020 estimates under Scenario I and the national average statistics [54]. National average values were calculated based on the data in Table 1. The estimated annual CO₂ emissions per private vehicle were 2.37 t-CO₂ in Maniwa City and 2.18 t-CO₂ in Hidakagawa Town, corresponding with differences in daily average driving distances. Given the larger land area of Maniwa City compared to that of Hidakagawa Town, the average vehicle travel distances are longer, leading to higher CO₂ emissions from private vehicles. In addition, the estimated results for both regions exceeded the national average of 1.86 t-CO₂. Specifically, estimates for Maniwa City and Hidakagawa Town were 1.3 and 1.2 times higher, respectively, than the national average. Overall, due to severe depopulation and limited access to public transportation, hilly and mountainous areas exhibited higher CO₂ emissions from private vehicles compared to the national average.

5.3. Regional Comparison of per Capita CO₂ Emissions Under Different Scenarios

Overall, significant reductions were observed in the total energy consumption and total CO₂ emissions in all the scenarios for both regions, driven primarily by population decline. Changes in per capita CO₂ emissions were discussed to facilitate comparative analysis. Figure 7 compares the estimated per capita CO₂ emissions in Maniwa City and Hidakagawa Town from 2020 to 2050 under the six scenarios.

Figure 7a presents the results for Scenarios I and II, which show that, in 2020, per-capita CO₂ emissions in Maniwa City were 3.62 t-CO₂, higher than the 3.16 t-CO₂ in Hidakagawa Town. This result is consistent with that of Maniwa City, which has a lower temperature and longer daily travel distance. By 2050, under Scenario I, per capita CO₂ emissions in Maniwa City and Hidakagawa Town were projected to decrease to 3.15 and 2.97 t-CO₂, respectively, reflecting a reduction of approximately 10%, yet still indicating high emission levels. Additionally, in Scenario II, where intra-regional migration measures were implemented, an increased rate of housing renewal resulted in further reductions, with per capita CO₂ emissions reaching 2.98 t-CO₂ in Maniwa City and 2.76 t-CO₂ in Hidakagawa Town. The results for Scenario II achieved additional CO₂ emission reductions of 4.8% and 6.5% for Maniwa City and Hidakagawa Town, respectively, compared to those in Scenario I, highlighting the emission reduction potential of population migration. In particular, Hidakagawa Town, where population centralization is more pronounced, showed a more significant reduction effect.

Figure 7b presents the results for Scenarios III and IV, which assumed that progress in the introduction of various measures will remain at the current level. In 2050, under Scenario III, per capita CO₂ emissions were projected to decrease to 2.02 t-CO₂ in Maniwa City and 1.80 t-CO₂ in Hidakagawa Town, achieving reduction rates of 41.5% and 40.1%, respectively, compared to emissions in 2020. Scenario IV, which combined migration measures with current trends, proved even more effective, with reduction rates of 45.4% and 45.9%, respectively. Sectoral analysis showed similar trends in both regions, with reductions in the residential sector reaching approximately 70% by 2020. Furthermore, the projected PV installation rate in 2050 under Scenario III will be 24.9% in Maniwa City and 29.3% in Hidakagawa Town, indicating greater expansion of residential PV adoption in Hidakagawa Town, following existing trends. However, the reduction rate in the transportation sector was only approximately 20% in both regions, mainly because of extremely low EV adoption and growth rates. Overall, these results suggest that, under the current trend, it is possible to achieve a 40% decrease in per capita CO₂ emissions by 2050 compared to 2020 levels in both study areas.

Figure 7c presents the results for Scenarios V and VI. Per capita CO₂ emissions declined progressively in the transportation sector with increased EV adoption, reaching zero by 2050 under full EV penetration. On the other hand, per capita CO₂ emissions in the residential sector under Scenario V are projected to drop to 0.03 t-CO₂ in Maniwa City and 0.02 t-CO₂ in Hidakagawa Town. Furthermore, the PV installation rate will have significantly increased, reaching 50.6% in Maniwa City and 54.2% in Hidakagawa Town. In Scenario VI, per capita CO₂ emissions were further reduced to 0.02 and 0.01 t-CO₂, respectively, demonstrating significant reductions. Overall, in Scenarios V and VI, which targeted the decarbonization of residential and transportation sectors, the strengthening of various measures enabled a reduction of >99% in per capita CO₂ emissions by 2050 in both study areas, suggesting that decarbonization is achievable.

According to the results, by 2050, Maniwa City is expected to have a population of 22,288 and 9123 households, and Hidakagawa Town is projected to have 4125 residents and 1894 households. Both regions are predicted to experience a continuous population decline. Regarding the population decline rates, by 2030, Maniwa City and Hidakagawa Town are expected to decline by 19% and 20%, respectively, compared to that in 2020. Thereafter, the pace of population decline in Hidakagawa Town is projected to accelerate, reaching a 55% reduction by 2050, which is higher than the 48% decline in Maniwa City. In addition, the aging rate in both regions is expected to increase annually, reaching 48% in Maniwa City and 56% in Hidakagawa Town by 2050. The projected future trends indicate that both regions will experience increasingly severe population decline and aging. However, Hidakagawa Town is likely to face more severe consequences, particularly because of its smaller population, low population of young women, and population outflow.

Regarding changes in the future population distribution due to migration, by 2050, the remaining population of Maniwa City is expected to be concentrated primarily in the Kuse, Ochiai, Hokubo, and Kawakami districts. Among the nine districts, the proportion of settlements that disappeared was highest in Yubara, Mikamo, and Chuka. In contrast, the population of Hidakagawa Town is expected to become highly centralized in the western Kawabe district, with projections indicating that 92% of the total population will reside in this area by 2050.

6. Discussion

As analyzed in Section 5.2, the estimated CO₂ emissions from residential and transportation sectors in this study generally aligned with the national statistical data [29,54] in terms of both emission levels and regional differences. Matsuhashi et al. [23] estimated that annual per capita CO₂ emissions from the residential sector across municipalities in Japan generally ranged from 1.0 to 4.0 t-CO₂, with a particular concentration between 1.5 and 2.5 t-CO₂. Additionally, emissions from private vehicles were generally in the range of 0.4–1.4 t-CO₂. Under Scenario I, this study estimated 2.0 t-CO₂ and 1.8 t-CO₂ for the residential sector in Maniwa City and Hidakagawa Town, respectively, and 1.6 and 1.4 t-CO₂ for private vehicles. While residential sector results closely matched those of Matsuhashi et al., slightly higher values for vehicle emissions were observed. This difference likely stems from more detailed local data used in this study [44,49], compared to the prefectural-level data in [23], which may underestimate emissions in areas with high car dependency. Therefore, this study enriches the detailed data on vehicle mileage and ownership in small municipalities, enhancing the regional representativeness and reliability of the results. Furthermore, these studies confirm that the residential and transportation sectors in Japan’s hilly and mountainous areas currently exhibit high energy consumption and CO₂ emissions, largely driven by regional challenges such as depopulation and aging, population outflow, aging buildings, and inadequate public transportation. Additionally, the differences between Maniwa City and Hidakagawa Town suggested that CO₂ emissions were influenced by various regional characteristics such as climate, resources, economy, demographics, and lifestyle patterns. The high energy consumption and CO₂ emissions in these areas may exacerbate environmental and resource depletion, further exacerbating various regional issues. Therefore, it is crucial to consider effective decarbonization strategies tailored to diverse regional characteristics.

This study examined the effectiveness of multiple strategies for reducing the CO₂ emissions in Japan’s hilly and mountainous areas, focusing on technological advancements and demographic spatial patterns. The findings indicate that the expanded adoption of PV, ZEH, and EVs, settlement concentration, improved housing performance, and reductions in CO₂ emission factors have contributed to the CO₂ reduction in both regions. A nationwide analysis [28] also confirmed the effectiveness of deploying existing technologies—especially highly insulated buildings, efficient water heaters, and PV systems—for decarbonization in the residential sector. However, these studies also reveal the insufficiency of the current intensity of countermeasure implementation. The present study indicated that under the current trend (Scenario III), projected per capita CO₂ reduction rates by 2050 were 41.5% in Maniwa City and 40.1% in Hidakagawa Town, with the residual CO₂ emissions remaining high. The transportation sector, in particular, presents greater challenges due to persistently low EV adoption, which is expected to remain under 2% of households by 2050. Meng et al. [68] also pointed out the lag in Japan’s transport decarbonization compared to leading countries such as China and the United States, where EV diffusion is far more advanced. In addition, Kyoto City estimated a 38% per capita CO₂ reduction under current trends by 2050 [69], while Yusuhara Town (i.e., another hilly and mountainous area) projected a 43% reduction [31]—both falling short of carbon neutrality. Despite the differences in the analytical sectors and baseline years, all the above studies indicate that achieving carbon neutrality by 2050 will be challenging under the current trend. In the present study, under the more aggressive decarbonization scenarios (Scenarios V and VI), decarbonization was deemed feasible. Therefore, advancing regional decarbonization will require the enhancement of measures targeting both energy supply and demand in the future.

As discussed in Section 3.3.5, both study areas have set decarbonization goals and implementation plans at the policy level. In terms of financial support, various subsidy programs have played a vital role in promoting the adoption of PV, ZEH, and EVs, with recent years showing a steady increase in the adoption rates in both regions. However, as many measures are closely linked to residents’ consumer habits and personal preferences, policy and financial incentives alone may not ensure widespread implementation. Thus, enhancing public awareness and fostering a sense of responsibility toward decarbonization and sustainability are essential. Beyond providing technical insights through modeling and scenario analysis, this study also emphasized the social dissemination of findings through engagement in local citizen meetings and collaboration with municipal governments [70]. The results of a questionnaire survey revealed that citizens in proactive regions like Maniwa City exhibit higher awareness and acceptance regarding decarbonization, environmental issues, policy support, renewable energy adoption, and regional contribution [71]. Overall, the coordinated effects of policy guidance, economic incentives, technological measures, and public engagement suggest a feasible path for strengthening decarbonization efforts in Japan’s hilly and mountainous areas.

Regarding the effectiveness of introducing measures in different regions, the effects of intra-regional migration measures are first discussed. The migration measures proposed in this study showed a certain amount of CO₂ emission reduction in both study areas by promoting housing updates and shorter travel distances through population concentration. However, these effects are weaker than those of technological measures. Under Scenarios I and II, migration measures could achieve additional per capita CO₂ emission reductions of 4.8% in Maniwa City and 6.5% in Hidakagawa Town by 2050. Under the current trend scenarios (Scenarios III and IV), additional reductions of 3.9% and 5.9%, respectively, were expected. Hidakagawa Town showed greater effectiveness due to clearer population concentration in the urban center (Kawabe). In addition to contributing to the decarbonization of the residential and transportation sectors, regional concentration can also enhance urban services and improve the living satisfaction of residents. As differences in effectiveness can be observed between the regions, when considering the establishment of compact cities in response to depopulation, it is necessary to comprehensively assess the structure of each region and the distribution of the population and facilities. Moreover, technological measures also varied by region: energy-efficient retrofitting was more impactful in colder Maniwa City, while PV systems tended to meet the electricity demand more easily in Hidakagawa Town due to the lower demand. These findings underscore the importance of considering regional characteristics when implementing measures.

Following up, it is necessary to discuss the limitations of the present study. (1) This study focused on Maniwa City in the Chugoku region and Hidakagawa Town in the Kinki region as representatives of Japan’s hilly and mountainous areas. Given Japan’s extensive north–south span and the resulting regional diversity, significant variations in energy use and CO₂ emissions were anticipated. Thus, this study had certain limitations in terms of the number and diversity of target regions. (2) The feasibility of achieving regional decarbonization relies heavily on substantial reductions in future CO₂ emission factors, particularly for electricity and gas. This study assumed near-zero emission factors by 2050, which requires the widespread adoption of renewable energy. While technological advances have made progress, large-scale deployment still faces barriers including cost, infrastructure, public acceptance, and interregional cooperation. Moreover, as emission factors decline, the relative contribution of other measures (e.g., improved housing performance and settlement concentration) to total emission reductions will decrease, although these strategies remain necessary. (3) This research focused on CO₂ emissions during the operational phase of residential and transportation sectors, lacking a full life-cycle perspective. Additionally, in 2018, housing vacancy rates in Maniwa City and Hidakagawa Town reached 19.9% [72] and 20.3% [73], respectively. Given the projected population decline of approximately 50% by 2050 in both areas, a substantial increase in vacant houses is expected. In Maniwa City, a 2019 survey indicated that over 80% of vacant houses were potentially reusable—either immediately or after renovation [72]. Based on this ratio, more than 4000 houses are projected to be reusable by 2050. Considering the economic and environmental benefits of utilizing existing housing stock, promoting the reuse of vacant homes should be regarded as a practical and effective decarbonization strategy, which remains insufficiently explored. (4) While the study prioritizes decarbonization, it does not fully address regional revitalization. Sustainable development in hilly and mountainous areas should also include strategies such as promoting mountain tourism, circular use of local resources, and sustainable agriculture tailored to local contexts. Future studies should consider integrating low-carbon strategies with regional revitalization to better support both decarbonization goals and broader SDGs.

7. Conclusions

This study examined the energy consumption and CO₂ emissions in the residential and transportation sectors of Maniwa City, Okayama Prefecture, and Hidakagawa Town, Wakayama Prefecture, two typical hilly and mountainous areas of Japan. Focusing on the unique characteristics of each region, multiple scenarios were constructed considering factors from the perspectives of both technological progress and urban structure. These scenarios were used to evaluate the CO₂ reduction potential and the practicality of realizing decarbonization at the regional level.

The findings of this study indicate that the population challenges in Japan’s hilly and mountainous areas are expected to intensify. By 2050, the populations of Maniwa City and Hidakagawa Town are projected to decline by 48% and 55%, respectively, compared to 2020 levels. Particularly, Hidakagawa Town is expected to face more severe challenges due to its smaller population, lower proportion of young women, and continued population outflow. The common issues in these regions, such as depopulation and aging, population outflow, aging buildings, and inadequate public transportation, significantly impact regional energy consumption and CO₂ emissions. Currently, per capita CO₂ emissions from the residential and transportation sectors in these areas exceed both national and urban averages. Moreover, differences between Maniwa City and Hidakagawa Town suggest that various regional characteristics such as climate, resources, the economy, demographics, and lifestyle patterns strongly influence emission profiles and the effectiveness of countermeasures. For instance, colder winters and longer driving distances in Maniwa City contribute to higher emissions, whereas Hidakagawa Town, benefiting from its land-use configuration, has achieved greater emission reductions through more concentrated migration patterns. Although achieving carbon neutrality has substantial potential to advance sustainable regional development, under the current trends, projected per capita CO₂ reduction rates by 2050 are only 41.5% in Maniwa City and 40.1% in Hidakagawa Town, compared to 2020 levels, suggesting that achieving carbon neutrality by 2050 will be difficult without further action. In contrast, under Scenarios V and VI, which targeted the decarbonization of residential and transportation sectors and strengthening of various measures enabled a reduction of over 99% in per capita CO₂ emissions by 2050 in both study areas. These results emphasize the necessity for stronger, integrated decarbonization measures from both energy supply and demand sides, while highlighting the importance of region-specific strategies tailored to the local characteristics to effectively realize regional decarbonization.

Overall, this study revealed that carbon neutrality is attainable in Japan’s hilly and mountainous areas. However, it also highlighted serious regional challenges, such as depopulation, aging, and delayed development, which have resulted in relatively high current levels of CO₂ emissions, posing significant barriers to emission reduction efforts. The findings of this study support progress toward SDGs, particularly SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). Specifically, this study supports the achievement of SDG 7 by assessing the potential for introducing renewable energy in Japan’s hilly and mountainous areas, thereby promoting its future expansion. It also contributes to SDG 11 by integrating population challenges and regional decline into the decarbonization framework and verifying the effectiveness of the corresponding countermeasures. Furthermore, in support of SDG 13, the study identifies region-specific decarbonization strategies and provides a scientific basis for substantial future reductions in CO₂ emissions. Finally, to promote decarbonization and sustainable development in these regions, this study provides several suggestions for future policy implementation and measure introduction. Firstly, it is essential to expand the practical application of existing technological measures (e.g., the utilization of renewable energy, improvements in housing performance, and the adoption of EVs). This requires the strengthening of regulatory frameworks and subsidy programs at the policy level. To further promote decarbonization, public awareness should be raised through local outreach and engagement activities, thereby fostering social acceptance and behavioral change. Secondly, the present study offers more targeted reference data for small-scale regions where research and data on regional decarbonization remain insufficient. The observed differences among regions underline the importance of considering local characteristics and adopting region-specific strategies. Furthermore, the reduction in CO₂ emission factors is a complex societal challenge that requires broader, large-scale interregional collaboration and exploration.

This study also identified several key directions for future research. While it focused primarily on the environmental impacts of decarbonization measures, future work should adopt a more comprehensive approach, incorporating economic costs, regional feasibility, and residents’ convenience to assess the practical viability of policy implementation. Additionally, to fully support sustainable regional development and carbon neutrality, research should expand beyond the residential and private transportation sectors to include other areas such as the commercial sector. On the energy supply side, future studies should go beyond solar power and consider diverse renewable energy sources (e.g., biomass and wind power) to develop localized energy systems tailored to each region’s natural resource potential.