Proliferation of district heating using local energy resources through strategic building-stock management: A case study in Fukushima, Japan

Abstract

District heating systems using cogeneration technology and renewable resources are considered as an effective approach to resources conservation and reduction of greenhouse gas (GHG) emissions. However, wide-spread aging and depopulation problems, as well as the popularization of energy-saving technologies in buildings, are estimated to greatly decrease energy consumption, leading to inefficiency in district heating and barriers to technology proliferation. From a long-term perspective, land use changes, especially the progression of compact city plans, have the potential to offset the decrement in energy consumption that maintains the efficiency of district heating systems. An integrated model is developed in this paper based on building cohort analysis to evaluate the economic feasibility and environmental impact of introducing district heating systems to a long-term compact city plan. As applied to a case in the Soma Region of Fukushima, Japan, potential migration from the suburbs to the central station districts is simulated, where district heating based on gas-fired cogeneration is expected to be introduced. The results indicate that guided migration to produce concentrated centers of population can substantially increase the heat demand density, which supports a wider application of district heating systems and better low-carbon performance. These results are further discussed in relation to technology innovation and related policies. It is concluded that policies related to urban land use planning and energy management should be integrated and quantitatively evaluated over the long-term with the aim of supporting urban low-carbon sustainable development.

Keywords

building stock / compact city / district heating / energy use / Fukushima

Introduction

Excessive anthropogenic CO₂ emissions are considered to be the main cause of global climate change. Most CO₂ emissions are derived from fossil fuel combustion, for which electricity and heat production account for almost half [¹]. Not only is the promotion of energy saving activities important in reducing emissions from the energy sector, but increasing the overall energy conversion efficiency and proportion of renewable energies are also indispensable approaches. As compared with the expense of introducing carbon capture and storage (CCS) into large centralized fossil-energy-based thermal power stations, distributed energy systems using renewable energy sources, such as solar and wind power, have much lower costs and are recognized as ultimate solutions for decarbonization in the energy sector [²]. However, proportionally increased installation costs of high-capacity energy storage systems to avoid reverse flow to the grid will likely slow down the popularization of renewable energy sources [³]. Being an inclusive platform, distributed energy systems can blend various energy sources including fossil fuels, waste heat, and renewable energies based on combined heat and power generation (CHP or cogeneration) and heat recovery technology. As such, they play a critical role in supporting the transition from fossil fuels to renewables during energy restructuring. The feasibility and high efficiency of distributed energy systems has been reported in many published reports [⁴–⁸]. In addition, distributed energy systems also enhance the resilience of regional energy supply, becoming an important solution for climate change adaptation.

The Paris Agreement, which was adopted at the 21st Conference of the Parties of the United Nations Framework Convention on Climate Change (UNFCCC) in 2015, emphasizes the equal importance of both mitigation and adaptation measures in addressing climate change [⁹]. Being one of the main sources of CO₂ emissions, Japan has declared its intended nationally determined contribution (INDC), which targets to reduce GHG emissions to 26% of fiscal year (FY) 2013 levels by FY 2030 [¹⁰]. Owing to the impacts of the Great East Japan Earthquake in 2011, Japan has slowed down its transition from fossil energies to renewable energies. However, the popularization of distributed energy systems has increased based on a consideration of both emissions reductions and disaster prevention [¹¹,¹²]. In particular, since the introduction of distributed energy systems can develop local economies by using local resources and providing local jobs, they are considered to be important drivers of regional revitalization.

Since the 1990s, an increasing number of municipalities in Japan have focused on introducing district heating systems using local unused energy to provide local energy saving and security as well as economic growth and job creation. Practices in the national Eco-Town Projects (Zero-Emission Initiative) from 1997 have indicated the feasibility of waste heat recovery from thermal power plants and industries as a measure for resource recycling and emission reductions in industrial parks [¹³–¹⁵]. With the upgrading of this initiative to the “Eco-Model City” initiative and “Future City” initiative, many municipalities have attempted to introduce district heating in urban areas using waste heat and biomass fuel. However, a low heat demand density and limited budget in local cities are major obstacles that decision makers must overcome in district heating projects.

The previous literature indicates two approaches for enhancing the feasibility of district heating projects in low heat demand cities. First, to substantially reduce the heat loss rate, technical innovations on the supply side, such as better pipeline layouts, improved thermal isolation materials, and efficient heat exchangers, are expected to be of benefit [¹⁶–¹⁸]. Furthermore, according to real-time demand variations, an optimal combination of heat production technologies with appropriate operation schedules for a composite district heating system can substantially increase energy efficiency [¹⁹–²¹]. In particular, as the 4th generation of district heating (4GDH), district heating is now able to connect to more low-temperature heat sources such as solar heat and waste heat, which substantially enhances its feasibility and competitiveness compared with individual heating technologies [²²,²³]. In combination with hydrogen energy storage, solar and wind power can also connect to district heating through fuel cells [²⁴].

Demand side management is the second approach for enhancing the performance of district heating. Over the short-term, energy consumption monitoring and demand response through ICT (information and communication technology) and IoT (internet of things) systems among building clusters is an effective measure for peak shaving [²⁵,²⁶]. Over the long-term, land use changes are critical to the feasibility of expanding district heating networks. In satisfying the requirement of minimum linear heat density [²⁷], compact land use in residential areas near industrial parks would help to minimize the initial investment and operation costs while maximizing the benefit of using waste heat [²⁸–³⁰]. In particular, the emerging planning method of Urban and Industrial Symbiosis, provides a comprehensive approach for promoting a multi-source heat exchange network between industries and urban areas, thereby maximizing energy saving [¹³,¹⁵,³¹–³⁶]. In general, geographic proximity is a key factor to be considered in developing a heat exchange network.

The measures mentioned above could indeed increase the performance of district heating in the future. However, long-term building renewal focused on low-energy buildings has many uncertainties that are potentially significant in low-energy-density cities. For instance, the 2012 Law on Promoting Low-Carbon Cities promotes compact land use planning in all cities of Japan; this is a positive step toward introducing more district heating projects. In addition, following the Law on Promoting No-Telegraph-Pole Urban Design (Ministry of Land, Infrastructure, Transport and Tourism), the digging of common ditches for pipelines will become more common in Japanese cities, substantially reducing construction costs for district heating. However, the proliferation of ZEHs (Net-Zero Energy Houses) during building retrofitting may further decrease heat demand and make district heating uneconomic. To ensure the continued performance of district heating projects during long-term urban renewal, an integrated evaluation model is required to consider the uncertainties brought about by interacting policies and market changes. As a preliminary study, the present work aims to ① introduce a spatial building cohort analysis based on a 4-dimensionalgeographic information system (4D-GIS) to simulate retrofitting and technology proliferation in building stocks and ② evaluate the impacts of building stock management policies on expanding district heating networks in urban areas. The concept and general procedures of the 4D-GIS analysis can be found in Refs. [³⁷,³⁸]. The Soma Region in north-eastern Fukushima was chosen as the study area.

Study area

Geographic features of study area

The Soma Region in Fukushima Prefecture was chosen as the study area because this is a typical low energy density region that was seriously affected by the Great Eastern Japan Earthquake in 2011. As such, during the process of revitalization, local municipalities have shown a strong desire to introduce smart district energy systems using local natural gas resources as well as to conduct a corresponding brand new urban redevelopment plan to coordinate with the district energy systems.

As shown in Table 1, The Soma Region is in the north-eastern coastal area of Fukushima Prefecture which includes three municipalities: Shinchi Town, Soma City, and Minamisoma City (from north to south). According to the 2016 Statistical Yearbook of Fukushima, the Soma Region has a total land area of 640 km² with a population of around 103000. It had a gross regional product (GRP) of around 506 billion JPY in 2013, of which manufacturing accounted for 17%. Soma City possesses most of the manufacturing production in the region, benefitting from the location of a large-scale industrial park (Soma Core Eastern Industrial Park). In 2014, the shipment value of Soma City reached 175.8 billion JPY, while for Minamisoma City and Shinchi Town the values were 72.4 billion JPY and 10.5 billion JPY, respectively. Currently, a large-scale LNG (liquefied natural gas) base, which is expected to supply cheap gas energy for new industries and therefore aid job creation, is under construction in the industrial park. Effectively exploiting the advantages offered by this location for industrial development is currently of significant interest in relation to revitalization.

By contrast, Minamisoma City has a greater population than do Soma City and Shinchi Town. Generally, residents distribute along the Joban railway line, especially centering around Soma station, Haranomachi station, and Odaka station. With the construction of the LNG base, the introduction of a district heating network using gas-fired cogeneration was proposed by the authors of the present paper. As such, a pilot project has been launched in the newly developed residential district near Shinchi station. In this project, an automatic demand response (ADR) using tablets to visualize household energy consumption is in use [¹⁹]. Compared with Shinchi station, the introduction of such technologies to the Soma and Haranomachi station districts should be more feasible. This is one of the main practical applications of this study.

Using the same method used in the previous case study of Shinchi Town [²⁸], the heat demand (including space heating and hot water demands) is estimated. The annual total heat demand of Soma Region is estimated at 3100 TJ. Following the distribution of building stocks, the heat demand distribution is relatively concentrated around Soma station and Haranomachi station. Most of the rest of the areas have a heat demand of lower than 0.04 TJ/km², a level at which it becomes infeasible to introduce a district heating network in Japan. In addition, there is a large amount of waste heat generated in Soma Core Eastern Industrial Park; this could be utilized as a heat source for district heating in the Komagamine station district [²⁸,³⁹].

Regional future perspectives

Fukushima Prefecture is suffering from serious depopulation and an aging population; Soma Region is no exception. According to predictions by the National Institute of Population and Social Security Research, the population of Fukushima will decrease by 1% annually from 2 million in 2010 to 1.5 million in 2040 [⁴⁰]. However, if planned economic growth is realized in Soma Region, there is a possibility of net immigration and maintenance of the regional population. By applying a snapshot model, the authors predict potential economic growth based on the prefectural Basic Plan for Promoting Industrial and Commercial Revitalization [³⁹,⁴¹]. Currently, the national and prefectural governments are implementing the so-called Innovation Coast Initiative to accelerate the revitalization of the coastal region by ① encouraging new industries including those providing renewable energy, ICT, IoT, artificial intelligence, and medical instruments and ② demonstrating the application of such industries in local smart community projects. By popularizing mega-solar panels, offshore wind power, and biomass-fired and geothermal power generation, Fukushima is targeting at a 100% renewable-energy-based society by FY 2040.

On the other hand, Soma Region is also promoting compact land use. Although the three municipalities in Soma Region have not yet declared their own Plans for Promoting Optimal Location (as advocated by the Ministry of Land, Infrastructure, Transport and Tourism of Japan), they have proposed similar Development Plans for Revitalization to promote compact land use. Shinchi, Soma, Kashima, Haranomachi, and Odaka station districts have been chosen as target areas for population concentration. The mechanism of the Location Optimization Plan is to induce the relocation of residents and urban services to target areas through the relocation of critical public facilities including regional hospitals, sports centers, shopping centers, schools, and entertainment. Economic incentives such as location subsidies are given to those critical facilities that follow the guidance of the master plan. In Shinchi Town, households that relocate to the station district are also eligible for location subsidies. In addition, both Shinchi Town and Minamisoma City have been selected as “Future Cities” under an initiative of the Cabinet Office so that their endeavors in promoting compact land use and distributed energy systems using local resources will be used for demonstration purposes to other municipalities across the country.

Notably, the authors’ research group has led one of the smart community projects located in Shinchi Town, which is now in its implementation stage. Based on gas-fired cogeneration using nearby industrial LNG resources combined with solar and wind power networks, energy monitoring and demand response devices have been introduced in smart buildings near Shinchi station, where compact and mixed land use design is used to provide an optimal matching of energy supply and demand (Fig. 1) [¹⁹,²⁸,⁴²]. As an extension of these attempts, this study aims to consider the feasibility of popularizing such smart community plans across the whole prefecture. As the energy platform for smart communities, the feasibility of introducing district heating is the most important issue.

Model development

Model framework and data sources

As a first stage, this study focuses on integrating future land use scenarios into energy planning. Accordingly, both land use models and energy planning models should be taken into consideration, where land use changes should drive energy planning changes (a causal relationship).

As demonstrated in Fig. 2, this study develops a land use scenario model based on urban renewal strategies. The strategies are established according to the master plan of municipalities; however, the progression speed and anticipate deffects of the strategies correspond to realities such as population changes and building stock lifespan. Starting from the initial distribution of the building floor area, building survival rate is estimated by using the 4D-GIS analysis to determine the extent of building retrofitting, which is reduced by the ratio of depopulation. The spatially-adapted cohort analysis is applied for population and building simulation. In this part of the study, the aim was to simulate the future distribution of building stock and input the results into a technical assessment for energy planning.

Next, a technical assessment of energy planning is conducted where the service area is adopted as the key indicator for determining the supply quantity of district heating systems. Based on a simple relationship, i.e., that compact land use will lead to a higher plot ratio and increase heat demand intensity, the minimum plot ratio required for the introduction of district heating systems are estimated and the service area is identified from the distribution of building floor area. Accordingly, the district heating performance, such as CO₂ emissions reduction, economic costs and benefits, and employment, can be evaluated.

In summary, the main assumptions in this study are as follows:

(1) Migration mechanism. When a building reaches the end of its lifespan, the owners (if they are still alive) can either rebuild the building in the same location or at another location, otherwise the building will be demolished immediately. To simplify the calculation, the inter-regional and the inter-municipal migration are not considered in this study.

(2) Location policy. Only two location activities of the building owners are allowed: ① to follow compact city plan (concentrate nearby regional rail stations) or ②to locate in the same place. To simplify the model, this study also assumes that the proportions of these two actions are fixed at a certain rate in the future.

(3) Duty for connection. If an area is feasible and reserved to introduce district heating network, all the buildings will be connected to the network and served by district heating. In addition, district heating service is assumed to be profit, meaning that annualized costs should be no more than annual profit.

(4) Spatial resolution. The structure, height, and scale of each building will lead to different heat demand densities which makes the model too complicated, thus this study roughly aggregates the floor area of all types of building stock into 100-m meshes which meets the scale of existing district heating projects in Japan.

(5) Solution to unoccupied houses. When depopulation goes faster than building abandonment, unoccupied houses appear and will not generate energy consumption. This study assumes that the unoccupied houses occurred in a period will be immediately filled by immigrants, which are in proportion to the quantity of building retrofitting in other areas (such as the effect of compensation for migrating to reuse unoccupied houses).

To realize the analytic framework shown above, a 4D-GIS database is proposed to be developed that is joined with other data sets for geographic analysis and technical assessment of district heating systems. The most critical aspect of this approach is to collect yearly maps of building distribution. In this study, the polygon data of building distribution were adopted, as provided by ZENRIN Co., Ltd., which were collected between 2010 and 2015. These polygon data are first aggregated into 100-m meshes. Population changes were estimated based on prefecture-level simulation results from the National Institute of Population and Social Security Research. The geographic information is supported by the ArcGIS data (2016) provided by ESRI Co., Ltd. and the database published on the National Land Numerical Information Download Service. Furthermore, the survey data from the report of “FY2017 CO₂ Technology Assessment Promotion Commissioned Program” by the Ministry of Environment of Japan were also referred to for assessment of district heating technologies. Other references are individually cited in the remainder of this section.

4d-GIS database and building lifespan estimation

Preparation of the 4d-GIS database and estimation of the buildings’ lifespan are the basic components of the analytic framework in this study. Here the concept “lifespan” means the actual value observed in previous building distribution changes. Currently, building distribution GIS database of Fukushima Prefecture for the year of 2010 and 2015 were collected, which are input into a hybrid method combining with the statistical data of building age distribution changes from the city yearbooks. According to the changes of observed number of buildings in different age, it is assumed that the buildings’ lifespan follows a Weibull distribution of which the parameters are calibrated by cumulative hazard function. The detailed calculation process can be found in Ref. [⁴³]. In this case, the Weibull distribution equation of the building survival rate is estimated as

(1)F(t)=\mathrm{1}-{\mathrm{e}}^{-{\left(\frac{t}{\theta }\right)}^m},

where t is the building age and m and θ are parameters estimated to be 1.772 and 51.868, respectively. Due to this result, the survival and abandonment rates during 5-year periods are estimated, as depicted in Fig. 3; these rates are applied to all buildings in this study. On average, the lifespan of the buildings is around 40 years.

Next, the building floor is aggregated into 100-meshes and the average building age for each mesh is estimated through detecting previous demolition activity from GIS database. In detail, by comparing the polygon data of building distribution in 2010 and 2015 using the select-by-location tool in ArcGIS desktop, which helps to automatically identify shape changes in the features between two layers), the changes of building location are classified into three categories: newly built, which appeared for the first time in 2015; demolished, which disappeared from 2010; and no change, which maintained the same location and shape during the time period. Due to this operation, the demolition rate for each mesh during the most recent 5 years is calculated and input into the estimated survival rate curve to determine its most likely building age. In this case, the rate of demolished buildings is found to be 18.1% over the 5 year period, whereas the rate of building renewal is slightly lower at 17.4%. For the demolished buildings, 54.4% were rebuilt in the same mesh area, meaning that the rest were removed or relocated. Notably, in calculating the plot ratio of each mesh, the area covered by river and water, as well as the part for railway, road and other surface infrastructure construction were excluded.

Mechanism of migration and building relocation

As mentioned above, migration is assumed to happen when the house is demolished earlier than the life end of the owner. Learning from the previous building distribution changes, around 54% of demolished floor area are renewed in the same location. Accordingly, this study applied a simplified decision tree to predict demolition of current floor area and rearrange the rebuilt part into the identified concentrated living districts. The image of building retrofitting and relocation in compact city planning is displayed in Fig. 4.

Figure 5 summarizes the calculation flow of next-term floor area distribution based on the mechanism of compact city planning. First, rebuilding activity only happens when demolition rate is over depopulation rate in a mesh area, otherwise unoccupied floor area will appear. The unoccupied part will be immediately filled in by the migrants from other meshes proportionally, otherwise the oldest houses in the region will be demolished equally to confirm the market clearing. Then, if vacancy does not happen in the mesh, the floor area which needs to be supplemented will be estimated after deducting the part for depopulation and vacancy in other meshes. If the mesh is identified for concentrated living according to the city’s master plan, abandoned floor area will all be immediately rebuilt in the same location, otherwise part of them will be relocated in other meshes which are identified for concentrated living. Whereupon, floor area will be added for the latter to reach required plot ratio for introducing district heating network. The method for determining the minimum plot ratio is described later.

Accordingly, the rule for allocating the next-term building floor area is simply defined as

(2)a_{i,t+\mathrm{5}}={\displaystyle \sum _y}{a}_{i,t,y}-(\mathrm{1}-\mu )\cdot {\displaystyle {\sum }_y}(a_{i,t,y}\cdot {\sigma }_{y,t})-\mu {\displaystyle \frac{{\displaystyle {\sum }_y}(a_{i,t,y}\cdot {\sigma }_{y,t})}{{\displaystyle {\sum }_{i,y}}(a_{i,t,y}\cdot {\sigma }_{y,t})}}({\displaystyle \sum _{i,y}}{a}_{i,t,y}\cdot \lambda )+\theta \cdot (\bar{\rho }-{\rho }_i)\cdot A_i,

where a_{i,t,y} is the floor area at age y of mesh i in the year t, {\sigma }_y is the 5-year demolition rate of the floor area at age y, \lambda is the overall rate of depopulation, ρ_i is the plot ratio of mesh i, \bar{\rho } is the target plot ratio, μ is the overall retaining rate of the floor area, and A_i is the area of mesh i. As it is assumed that the proportion of relocated floor area remains the same, μ = 54% if mesh i is in the suburbs and \mu =\mathrm{100}\% if mesh i is in the regional station districts. Furthermore, θ = 0 if the mesh is not required to reach the target plot ratio and θ = 1 if the mesh is required to reach the target plot ratio. The depopulation rate in Fukushima during each 5-year period from 2015 to 2040 are 2.04%, 4.98%, 5.38%, 5.80%, and 6.39%, respectively [⁴⁰].

Feasibility conditions for district heating

Design and establishing conditions of a district heating system

The district heating system proposed in this study consists of current common technologies including gas engine to generate heat and power and pipeline network to transport heat (hot water) with pumping station to keep water pressure. Heat is directly sold to the users while electricity is sold to the grid. Here, the heat price is set as regional average price in North-eastern Japan that is independent to district heating project. Plot ratio is adopted as a typical parameter to link land use and heating system, where usually heat demand is increasing faster than pipeline construction corresponding to plot ratio increase. Thus, in a low-energy region, if annualized benefits equal to annualized costs when its plot ratio meets the required minimum value, district heating system will be introduced.

Evaluation of economic cost-benefits and emission reductions

The economic costs considered in this study include gas purchase cost, annualized pipeline and equipment investment, annual labor payments, and maintenance costs. The gas purchase cost for mesh i, and C_g,i, is calculated as

(3)C_{\mathrm{g},i}=p_{\mathrm{g}}\cdot q_{\mathrm{g},i}=p_{\mathrm{g}}\cdot {\displaystyle \frac{q_{\mathrm{h},i}}{(\mathrm{1}-\mathrm{\zeta })\cdot \eta \cdot v}},

where p_g is the price of natural gas, q_g,i is the gas consumption in mesh i, q_h,i is the heat demand in mesh i, ζ is the heat loss rate, η is the heat recovery rate for district heating, and v is the heat value for natural gas.

The annualized pipeline investment (distribution cost) in mesh i is defined as

(4)C_{\mathrm{d},i}=c_{\mathrm{d}}{l}_{i}a=c_{\mathrm{d}}{\displaystyle \frac{A_i}{\overline{\omega }}}{\displaystyle \frac{\lambda }{\mathrm{1}-{[\mathrm{1}/\mathrm{1}+\lambda ]}^{\tau }}},

where C_d,i is the annualized pipeline investment (distribution cost), c_d is the average pipeline installation cost, a is annuity, λ is long-term interest, and τ years is the legal duality of a cogeneration system.

Similarly, the annualized equipment investment cost in mesh i (C_e,i) is defined as

(5)C_{\mathrm{e},i}=c_{\mathrm{e}}\cdot P_{\mathrm{e}}\cdot a=c_{\mathrm{e}}{\displaystyle \frac{q_{h,i}\cdot \epsilon }{(\mathrm{1}-\zeta )\cdot \eta \cdot t}}\cdot {\displaystyle \frac{\lambda }{\mathrm{1}-{[\mathrm{1}/\mathrm{1}+\lambda ]}^{\tau }}},

where c_e is the purchase price of gas engine per kW, P_e is the required power output of a gas engine, \epsilon is the proportion of total annual heat demand in winter (half a year, as in a previous study in Shinchi Town), and t = 4380 h is the operation time over half a year.

The annual labor payment C_l,i and maintenance cost C_m,iare simply defined as

(6)C_{\mathrm{l},i}=c_{\mathrm{l}}{A}_i{h}_{\mathrm{l}},

(7)C_{\mathrm{m},i}=C_{\mathrm{g},i}+C_{\mathrm{d},i},

where c_l is the average annual salary per employee and n_l is the average number of employees per service area. Here, the maintenance cost is considered to be the same as the sum of annualized pipeline and equipment investment cost according to the database provided by the Agency for Natural Resources and Energy.

By contrast, the economic benefits considered in this study include heat-sales revenue R_h,i and power-sales revenue R_e,i, defined as

(8)R_{\mathrm{h},i}=p_{\mathrm{h}}{q}_{\mathrm{h},i},

(9)R_{\mathrm{e},i}=p_{\mathrm{e}}{\displaystyle \frac{q_{\mathrm{h},i}\cdot \mu }{(\mathrm{1}-\zeta )\cdot \eta }}(\mathrm{1}-\delta -\kappa ),

where p_h is the price of heat sales, p_e is the price of electricity sales, μ is the power generation efficiency of a gas engine, \delta is the proportion of power generated for pumping hot water, and \kappa is the proportion of power generated by operating other equipment in heat and power stations.

Furthermore, assuming that the current heat consumption for space heating and hot water are supported by air conditioners and gas boilers, respectively, the CO₂ emissions in the current situation, E_BAU,i are calculated as

(10)E_{\mathrm{BAU},i}=q_{\mathrm{h},i}{\displaystyle \frac{\gamma }{\psi }}{\epsilon }_{\mathrm{e}}+q_{\mathrm{h},i}{\displaystyle \frac{\mathrm{1}-\gamma }{\zeta }}{\epsilon }_{\mathrm{LPG}},

where \gamma is the proportion of heat consumption for space heating, \psi is the COP (coefficient of performance) of the air conditioner, \varsigma is the efficiency of the gas boiler, {\epsilon }_e and {\epsilon }_{\mathrm{LPG}} are the emissions factors for grid electricity and LPG (liquid propane gas), respectively. Accordingly, the CO₂ emission reduction obtained by introducing district heating can be calculated as the sum of substituted CO₂ emissions in the current situation and that of substituted power generation after subtracting the increasing CO₂ emissions from gas consumption for cogeneration, as shown in Eq. (11).

(11)\mathrm{\Delta }{E}_i=E_{\mathrm{BAU},i}-{\displaystyle \frac{q_{h,i}}{(\mathrm{1}-\zeta )\eta }}{\epsilon }_{\mathrm{NG}}+{\displaystyle \frac{q_{\mathrm{h},i}\mu }{(\mathrm{1}-\zeta )\cdot \eta }}(\mathrm{1}-\delta -\kappa ){\epsilon }_{\mathrm{e}},

Relationship between plot ratio and total length of pipelines

According to Refs. [²⁷,⁴⁴], effective width is used to estimate the length of pipeline in an area based on plot ratio, which is defined as

(12)\bar{\omega }={\displaystyle \frac{A_i}{l_i}}=\alpha e^{\beta (a_i/A_i)},

where l_i is the estimated pipeline length in mesh i, A_i is the land area of mesh i, a_i is the building floor area in mesh i, and α and β are parameters. From the manual of district heating projects in Japan [⁴⁵], those projects selling heat to both residential houses and commercial buildings are screened out and α = 133.7 and β = –0.613 are estimated by regression. These results are relatively high compared with the reference value obtained for Sweden.

Minimum plot ratio for introducing district heating

To find out the minimum plot ratio of a mesh for introducing district heating, first it is necessary to assume a minimum heat price to balance annual costs and revenue. Using the formulas mentioned above, the changes of minimum heat price in different plot ratios can be calibrated. Figure 6 is an example when the average piping cost is 200000 JPY/m. Then, corresponding to the average heat price set in the case region, the minimum plot ratio can be calibrated. Learning from the statistical yearbook of district heating projects in Japan, the average heat price in existing district heating projects is around 4.4 JPY/MJ, for which the required minimum plot ratio should be 70% [⁴⁵]. However, if the average piping cost is too high, a heat price of 4.4 JPY/MJ may not be able to balance costs and benefits.

All parameters used in this study are summarized in Table 2.

Scenarios setting

As summarized in the model developed above, the factors which may have an impact on the proliferation of district heating network are categorized as: population change, which determines the total quantity change of building floor area; compact city planning, which arranges the next term building distribution to concentrate into identified station districts; lifespan of buildings, which affects the speed of building retrofitting; heat demand intensity, which represents the unit energy consumption by floor area; supply technology innovation, which occurs in near future that enhances the efficiency of district heating; and changes of units for project evaluation in near future that impacts on the competitiveness of district heating to individual heating technologies. These 6 factors are separated or combined into 8 scenarios as summarized in Table 3.

The effect of compact city planning on district heating network expansion can be evaluated by comparing Business as Usual (BAU-BL) and Compact Land Use (CLU)-BL. Then, to track the joint impact of the other 4 factors on compact city planning, 4 scenarios (CLU-LL, CLU-HI, CLU-SI, CLU-UC) are set separately, by assuming that only one of them will happen in the future, while the other 2 scenarios (BAU-ALL, CLU-ALL) by assuming that none of them or all of them will happen.

Results and discussion

Trend of regional heat demand in scenarios

The changes of regional total heat demand in scenarios are exhibited in Fig. 7. Because population decrease is the only factor affecting the heat demand density in scenarios BAU-BL, CLU-BL, CLU-LL, CLU-SI, and CLU-UC, the total heat demand in such scenarios reveals the same decrease with depopulation rate from 3074.6 TJ in 2015 to 2387.5 TJ by 2040. In comparison, the heat isolation improvement in existing and new buildings is found to further reduce the heat demand to 2167.3 TJ by 2040 (CLU-HI). The contribution ratio of depopulation and heat isolation improvement are 75.7% and 24.3%, respectively. In addition, the policy of extending buildings’ lifespan is found to slightly decrease the heat demand reduction to 2190.0 TJ by 2040. In summary, the cities in depopulation trend should be likely to turn into low-energy cities in the near future.

District heating network expansion in different scenarios

Under the background of a quick reduction of heat demand in Soma Region, the feasibility of introducing gas-fired cogeneration-based district heating system was discussed considering compact land use planning and other policy implementation, as well as market trend. The results indicate that the feasibility of expanding district heating network can be confirmed through transiting the cities into a compact shape, but the actual performance on economic growth and environmental improvement is substantially affected by the trade-off between various policies and future market trend.

Figure 8 shows the changes of land area in service of district heating network in scenarios. Compared to current dispersed land use pattern (BAU-BL/BAU-ALL), district heating network is indeed expanding in identified compact living areas, but still quite limited if the other improvements are left out (CLU-BL). Next, the 4 separated scenarios clearly reveal the trade-off effect between the policies and market trend. In CLU-LL, because a large number of unoccupied houses appear after indistinguishably extending buildings’ lifespan, the space for adjusting land use becomes extremely limited which almost stops the expansion of district heating network. Similarly, heat isolation improvement decreases the speed of network expansion but still remains the feasibility (CLU-HI). However, this situation is substantially reversed after taking the other 2 positive factors into consideration. Caused by technical and market changes, the average piping cost is reduced while the revenue of selling electricity and heat increases a lot, and the potential of network expansion can reach 92 ha in CLU-SI and 122 ha in CLU-UC by 2040. Although in CLU-UC, the fuel price keeps increasing in the same path with the energy price, the doubled increment in revenue by combining heat and power generation completely covers the negative impact. Notably, the positive impact of assessment unit changes (market trend, CLU-UC) appears slower than supply side innovation (CLU-SI) but has a much larger positive effect in the long-term. As a whole, these 2 positive factors totally offset the negative impacts from the former 2 factors, which confirm an expectable network expansion to 82 ha by 2040 (CLU-ALL).

Interestingly, a regional disparity of network expansion in different station districts is indicated. Because Soma City has relatively lower plot ratio near station but similar population scale, it is more sensitive to cost decrease in piping construction rather than price increase in electricity and heat. Therefore, Soma City shows a larger land area for introducing district heating in CLU-SI, meanwhile Minamisoma City shows a larger potential in CLU-UC which is more sensitive to electricity and heat price increment. However, extension of buildings’ lifespan causes more unoccupied houses in Minamisoma City, so that in the CLU-ALL scenario, the available land area for district heating is much lower than that in Soma City.

Economic and environmental performance of district heating system

This section compares the impact of introducing district heating on heating cost and CO₂ emissions. Here heating cost means the heat purchase cost in the user side. As indicated in Fig. 9, the annual heating cost is obviously polarized where BAU-ALL, CLU-UC, and CLU-ALL present a fast increment in heating cost. The rapidly increasing fuel and electricity price for performance assessment assumed in these scenarios should be the dominant reason, otherwise the heating cost should decrease with depopulation trend. Compared to baseline in compact city planning (CLU-BL) in 2040, extension of building lifespan slightly increases the heating cost by 0.1 billion JPY, the heat isolation improvement decreases the heating cost by 0.07 billion JPY, and the innovation in district heating decreases the heating cost by 0.5 billion JPY. However, the maximum heating cost reduction by district heating also appears in scenarios in which fuel and energy price changes are considered (CLU-UC), followed by the district heating system improvement (CLU-SI). Even assuming that all the assumptions will happen in the future, the cost saving by district heating still remains rapidly increasing to 500 million JPY by 2040.

By contrast, CO₂ emission reductions by district heating reveal an opposite trend with heating cost saving. Because of decarbonization in the power generation sector, the annual CO₂ emissions from heating decreases from 179.7 kt to around 86 kt (BAU-ALL, CLU-UC, and CLU-ALL) (Fig. 10). Compared to the level of CLU-BL in 2040, extension of building lifespan slightly increases the annual emission by 2.3 kt-CO₂, the heat isolation improvement decreases it by 1.7 kt-CO₂, and the innovation in district heating decreases the emissions by 11.0 kt-CO₂. As shown in Fig. 10, despite the fact that the supply side innovation (CLU-SI) brings an expected increment on emission reductions by district heating, the final result considering all the possible changes in the future reveals that the proposed district heating system (gas-fired cogeneration) is quite limited in CO₂ emission reductions. Although the economic efficiency of the proposed system is confirmed to strongly increase, it will lose environmental importance unless low-carbon heat sources, such as waste heat and renewables, are introduced.

Discussion

Learning from the results above, further discussions on the impacts from policies and future changes are summarized as below.

Lifespan extension of building stocks

Indistinctively extending lifespan of buildings will bring an indispensable barrier for promoting compact city planning in a depopulation region. In the case area, a 5-year demolition rate of buildings is estimated around 8% meanwhile the depopulation rate is around 5%, which means a potential of guiding the citizens to concentrate on regional station districts. However, a longer lifespan decreases the demolition rate even being lower than the depopulation rate, which brings a plenty of occupied houses. In addition, it also slows down the path of diffusing new technologies in new buildings and district heating, which reveals a negative effect on reducing regional energy in use. This policy can be improved by combining with compact city plan that discriminately implements in identified compact living area and the rest area of the city. The prediction on location and quantity of unoccupied houses is critical for delicacy stock management. Notably, embodied energy of buildings is not within the scope of this study which also brings a critical perspective on reducing energy consumption [⁴⁹,⁵⁰].

Enhanced heat isolation in buildings

Enhancing heat isolation in buildings is usually considered as an important measure to lower the use of air conditioner and save money. However, space heating only takes a relatively small proportion of the total heat consumption, while the technology also requires a certain time for popularization. In the case, yearly increasing low-energy buildings are indicated to have a certain negative effect on expanding district heating network, but not a determinant barrier if joining the other positive effects. However, the increasing investment intensity of district heating caused by enhanced heat isolation may further weaken the citizens’ willingness toward introducing district heating technology.

Innovation in district heating technologies

Technology innovation in district heating is indicated as a key factor for enhancing the competitiveness of district heating system, since the feasibility is quite sensitive to the costs for pipeline construction if given a certain heat price and plot ratio. Promoting co-groove construction in city center as well as extending the lifespan of pipelines and required pay-back period can substantially improve the economic feasibility of district heating. However, generally the investment intensity still remains high because district heating projects will always prefer taking place in areas with higher heat demand density where a denser pipeline network is also needed. In this regard, network expansion of district heating will be mainly limited by available budget.

Changes in project assessment factors

Predicted increment on fuel price is a negative factor to district heating projects, meanwhile increasing price of electricity and heat is indicated to substantially improve the economic competitiveness of district heating with air-conditioner and boiler. However, decarbonization in the power generation sector obviously decreases the low-carbon effect of district heating, which reveals an opposite trend with increasing cost-saving effect. Transiting natural gas into or combining it with other low-carbon heat sources, such as natural energy source and waste heat, is critical to keep the advantage of the district heating system. Otherwise, the economic advantage may also be weakened if the electricity price is effectively suppressed by cheaper production cost of renewable energy. This part should be carefully assessed in the prediction of future energy market changes.

Conclusions

The reduction of CO₂ emissions in the energy sector is of significance in relation to climate change mitigation. Corresponding to the shift in energy supply structure toward natural gas, renewables, and unused energy, district energy systems are expected to play a critical role in bringing these resources to users. However, the proliferation of district energy systems is affected by land use compactness, i.e., the location of households, industries, and public services. To promote responsible future urban planning, both land use and energy systems should be integrated with a consideration of population changes, building retrofitting, technology innovation, and even the units of accounting for evaluating pilot projects. Overall, planning should be implemented through reasonable strategic urban renewal. As a first stage, this study develops an integrated model to link land use simulation based on building cohort analysis with planning and an assessment of the impacts on the proliferation of district heating networks. In the case of Soma Region of Fukushima Prefecture, the results indicate that compact land use planning is critical to increasing plot ratios to meet the required heat demand density for expanding district heating networks. Considering all the factors which may affect the proliferation of district heating, compact land use has the potential to realize and increase the land area in service to 82 ha, with an increasing heating cost saving that reaches annually 507.2 million JPY by 2040. By contrast, the CO₂ emission reduction effect decreases from 1793.2 t to 950.1 t by 2040. To solve the trade-off between economic and environmental performance, it is indispensable to introduce low-carbon heat sources.

Policy implications can be learnt from the results; especially delicacy management for long-term urban renewal is highlighted herein, which takes a high spatial and temporal resolution. First, it is necessary to set a target plot ratio for districts where district heating network is expected to be connected. Considering the flexibility of required minimum plot ratio with the trade-off between policies, technology innovations and market changes, the target plot ratio should be carefully calibrated in a long-term perspective. Secondly, the target lifespan of buildings should be another key indicator for building stock management, which should be discriminated in geographic characters and optimal timing and well coordinates with policies in land use adjustment and building performance improvement. Finally, although district heating is indicated to keep advantage in economic benefits than individual heating, connecting to low-carbon heat sources like renewable and waste heat should be realized earlier, otherwise it may lose the advantage in CO₂ emission reduction. To ultimately realize the master plan for district heating, measures including choice awareness and transition management focusing on social aspects is a non-negligible issue for both public and private stakeholders [⁵¹,⁵²]. This study aims at quantifying the combined impacts from various policies that is expected to support the evidence-based policy making in integrated land use planning with energy system design.

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