3.4. Block C: Transition Measures to NZED
This Block comprises measures for decarbonisation that can be applied in a housing district. It includes 8 types of measures based on smart city technologies, building refurbishment, local renewable energy, and nature-based solutions. These are applied in various spatial entities and activities of a district, housing areas, mobility means, street network, public space open and green spaces. These are the following:
- C1.
Housing: Energy saving by building refurbishment
- C2.
Housing: Energy saving by smart city solutions
- C3.
Public lighting: Energy saving by smart city lighting
- C4.
Transport: Green mobility and energy saving
- C5.
Smart grid and storage
- C6.
Local RE: Photovoltaic panels
- C7.
Local RE: Heat pumps and geothermal heat pumps
- C8.
Nature-based solutions: Tree canopy and CO2 offset
The combined effect of the above measures and technologies should offset all CO2 emissions produced by using fossil energy, as described in Block B of the model, leading the city district into a net-zero state. All measures of block C (C1–C8) have an impact on the variables in Block B, either related to energy usage or CO2 emissions.
A very important metric in Block C is the coefficient (z) that measures the percentage of the population or households that adopt an NZED transition measure. The coefficient (z) applies to measures C1–C7 and its value is between 0 and 1. Per analogy, the percentage of the population that remains in the old behaviour is (1 − z). A value of (z) equal to zero means that no behaviour change has occurred, while a value of (z) equal to 1 is obtained when a measure becomes mandatory after being imposed by legislation.
Building refurbishment (improving buildings by re-equipping) or retrofitting (adding elements that the building did not have when first constructed) is about upgrading the energy system and performance of existing buildings. Building energy efficiency is the starting step towards an NZED. It ensures that buildings maximise energy efficiency, which in turn reduces the renewable energy generation to achieve an NZED.
Building energy retrofitting includes objectives of energy optimisation, lower environmental impact, and better living conditions in the building. Performance metrics may include energy reduction, Indoor Environmental Quality, CO
2 emissions, and financial such as energy bills and investment returns [
44]. Both refurbishment and retrofitting are implemented by improving or replacing lighting fixtures, ventilation systems, replacing single-glazed windows with double glazing, windows and doors, adding insulation on roof and external walls, especially for buildings that face direct sunshine which heat up quickly and need more energy to keep cool.
According to the EU news and information service
Science for Environmental Policy (
https://rb.gy/reecha) (accessed 13 December 2021), data from nine countries shows that building refurbishments and energy efficiency measures in existing housing districts could save 10% of energy for heating by 2020 and 20% by 2030. However, the country and local differences are high and depend on the quality of housing and climate.
A survey by Tuominen et al. [
45] shows high differences between countries and types of buildings in potential energy saving under the Energy Performance of Buildings Directive (EPBD) (
Table 6). The authors underline that their calculations “ rely heavily on average values for a large amount of buildings that are, in reality, very different. Some of the uncertainty is offset by the law of large numbers, i.e., even if some buildings are more difficult to renovate than average, others are easier, and in such a large sample both amounts are probably of more or less similar magnitude. Nevertheless, the results should be regarded as indicative estimates of a potential development, rather than exact forecasts” [
45], p. 50. The average reduction of heat energy is between 47.51% and 49.65% in houses and 45.38% in apartments. We should note that this high energy saving reflects the implementation of all EPBD measures, not building refurbishment only, according to targets of every country under the umbrella of the EU Directive on the energy performance of buildings.
Given this evidence, we estimate that potential improvements in energy performance by refurbishment is in the field of energy for heating. How much energy building refurbishment can actually save depends on local climate conditions and the quality of the building stock that define an energy reduction coefficient (x). For a coefficient x = 20%, which is the value proposed by the EU Science for Environmental Policy for 2030, the saving of energy will be equal to:
In smart cities, residential projects for energy saving have used smart meters and readable displays that allow users to become aware of energy consumption per electric appliance. A series of experiments and pilots in Amsterdam Smart City for assessing the contribution of smart city solutions to energy saving has led to rather mediocre results.
In the Geuzenveld neighbourhood, 500 homes have been provided with smart meters and displays that show energy consumption, while energy savings practices were discussed at citizen meetings and brainstorming sessions. Still, the energy savings per household were only 3.9%. In the West Orange project, 400 households have been provided with smart meters and displays that make it possible to see the energy consumption per appliance, and a personal energy-saving plan was set for every household. Energy savings per household were still only 7.8%. The ITO Tower, a pilot for testing energy savings in a large multi-tenant office building using smart building technology, smart plugs, and data analytics, saw a higher energy consumption fall of 18% [
46]. We should note that these data do not make it clear whether they refer to total energy or to energy consumption for lighting and appliances in the household. The overall ambition in Smart Amsterdam was to achieve energy savings at the level of 14%.
Simulations with an experimental smart home prototype that uses a microcontroller and various sensors for temperature and infrared for movement detection, as well as actuators to control the lights and air conditioning by Panna et al. [
47] show a much higher level of energy saving at the level of 20–30% depending on the number of persons per room.
Given this data, a conservative estimation is that smart home solutions can provide a 10% saving in residential energy consumption for lighting and appliances.
A city street lighting system consists of lighting poles, each consisting of a lamppost, a streetlamp, and other equipment depending on the type of lamps (e.g., ballasts when fluorescent lamps are used). Street lighting accounts for an important part of energy consumption, which is estimated between 15–40% of the energy spent in cities [
48,
49]. Smart city systems can considerably contribute to reduced energy consumption for street lighting and to net-zero objectives, especially when fossil energy is used to produce electricity.
At the level of a city district, the yearly energy consumption of a street lighting system depends on the number of poles in the district, the lamps wattage, and the system operating hours per year. The number of poles can be computed by the road length in the district. Calculations by Subramani et al. [
48] for a streetlight system with a spacing of 50 m and width 7 m, equipped with 250-watt lamps on each pole, shows illumination of 8.20 lumen per square meter, which exceeds the required illumination standard of 6.46 lumen. Thus 200–250 Watt lamps are adequate for streetlighting.
Optimising ordinary streetlighting with smart city solutions includes (a) replacing lamps with LED lights that have lower energy consumption, (b) installing sensors for motion detection, and (c) brightness adaptation for lights to switch on when pedestrians are near, or vehicles pass and switch off in the absence of movement. Thus, a lamppost works in four stages: off, low, medium, and high. Replacement of bulbs can be carried out with Light Emitting Diodes (LED) or Compact Fluorescent Lamps (CFL) which use 75% less energy than ordinary bulbs.
Using smart city lighting, it is estimated that the energy savings that can be made are around 50%. Following Subramani et al. [
48], p. 020082 “Replacing the existing lamps with LEDs, the required power is 5.33 kW so there is an energy saving of 12.61 kW for installed capacity and 5.43 kW for actual working in terms of kWh per year, the saving for installed capacity is 55,232 kWh and for actual working is 33,139 so the savings percentage is 57.7%”.
Escolar et al. [
50] conducted simulations in the city of Leganés, 11 km southwest of the centre of Madrid, a city equipped with 50,000 lampposts that “during winter nights turned on in state LOW at 6:00 p.m. and progressively increase the intensity to reach the HIGH state at 7:00 p.m. They remain in this state until 5:00 a.m., when they progressively decrease their intensity to reach the state OFF at 7:00 a.m. The energy savings reach 55% relative to the nonadaptive application”.
Nefedov et al. [
51], p. 1718 estimated that “LED technology enables intelligent street lighting that is based on sensing individual vehicles and dimming streetlights accordingly. The potential energy savings are considerable, exceeding 50% on roads with low traffic.”
Given this evidence, it is reasonable to accept that a traditional street lighting system can be upgraded to improve efficiency and reduce energy consumption by 50%, using LED and sensors so the light system glows and adapts upon detecting pedestrian or vehicle movement.
In mobility, energy consumption and CO2 emissions come from daily travel for work, shopping, recreation, and other activities. With the functional urbanism of 20th century cities, daily commuting is the rule for most of the population and substantial CO2 emissions are generated by daily travels. Urban sprawl and the integration of small settlements into metropolitan areas has increased commuting average travel distance and emissions.
New city planning concepts such as the 15-min city [
52,
53] can reduce daily commuting but will not make it disappear. In cities, many activities and land uses are unique (e.g., university campuses, museums, hospitals, polluting industrial estates, luxury commerce) and cannot be replicated in every cell of a 15-min city. Moreover, it is not feasible for every citizen to find a job close to their place of residence. On the other hand, work from home certainly opens a window of opportunity for such spatial concepts and telework will definitely contribute to lower commuting.
In daily commuting all available means are used: public transport, private cars, and green mobility (bicycles, electric scooters, electric vehicles). The distribution of transport modes is specific to each city, depending on culture, topography, transport means and infrastructure. In the transition to NZED, the objective for mobility is conversion to green mobility powered by renewable energy. In calculating the changes that can be introduced by green mobility measures, we use the following principles.
All energy and CO2 emissions for mobility are counted in the residential district of travel origin. As the commuting distance increases, energy consumption and emissions are released to neighbouring city districts. This calculation is the worst-case scenario for the district of travel origin but is neutral at the entire city level, as total emissions are aggregated from one district to another.
Within the next few years, public transport will progressively adopt electromobility, and all energy consumption (EMPT) will be covered by electricity. If only renewable energy is used, CO2 emissions (CMPT) will go down to zero.
Green mobility is on the rise and will continue to increase. The share of the population (z) that will adopt green mobility is specific to each city and should be introduced in the respective scenario.
However, a part of the population will continue to use conventional fossil fuel cars that release CO2. These emissions should be absorbed by nature-based solutions.
Thus, in the transition to NZED, we first estimate the energy needed for all kinds of electromobility, public transport, private cars, micro-mobility, which should be covered by renewable energy. The average energy consumption for electric vehicles is estimated at 0.2 kWh, 0.1 kWh/km for public transport, and 0.05 kWh/km for micro-mobility (
Table 5). The total electric energy depends on the distribution of commuting between public transport, electric vehicles, and e-micro mobility. Then, we compute CO
2 emissions from the use of fossil fuels cars, which depends on the yearly mileage, fuel consumption, and CO
2 emissions per unit of fuel. The amount of CO
2 released is estimated at 0.19 kg/km.
The smart grid is the backbone of the Net Zero Energy District. According to the U.S. Department of Energy, “these systems are made possible by two-way communication technology and computer processing that has been used for decades in other industries. They are beginning to be used on electricity networks, from the power plants and wind farms all the way to the consumers of electricity in homes and businesses” [
54]. Smart grids support many functions in the local energy system, such as integration of distributed energy resources located on buildings and other RE installations in the district, energy storage to secure uninterrupted supply of energy to users, and real-time monitoring of energy flows, enabling optimisation and service provision to producers and consumers.
Optimisation of energy supply and demand is important to avoid additional investments to cover peak loads in energy consumption. The smart grid of a city district can monitor and coordinate energy generation and consumption and reduce peak power demand. For instance, the so-called Virtual Power Plant (VPP) can compensate the volatility of renewable energy production through sharing among members of an energy community and energy storage in electric vehicles [
55].
Equally important is energy storage. In the context of smart cities, it can be conducted at different levels of the energy system: (a) at the energy generation level to balance and reserve power, (b) at the smart grid level to support capacity and investment deferral, (c) at the customer level to address peak load [
56]. Many engineering solutions and technologies can be used in RE storage such as compressed air, battery, pumped hydro storage plants, super capacitors, and flywheels. If RE is not supported by sufficient storage, it will not be effective. Balancing the volatility of RE generation to local consumption is the main task of smart grids and can be carried out by the grid storing quantities of energy. Energy storage levels at the smart grid level in Japan and Germany are 15% and 10%, respectively, and much lower in the US, at 2% only [
57].
Regarding the transition measures to NZED, the smart grid is a condition for the integration of the measures proposed (C1–C8), balancing RE supply and demand. The added value is estimated in the next two measures (C6 and C7) for local renewable energy.
In the model we propose, distributed photovoltaic panels are selected as the sole source of locally produced renewable energy. In non-self-sufficient NZEDs, additional renewable energy can be imported from external sources such as large-scale hydro, wind parks, and tidal installations. PV panels can be installed on all available buildings, private yards, and public spaces. They should be combined with storage for energy use when the sun sets, or with electric vehicles to store energy for mobility. An energy community will be needed to manage the distribution of energy among the households, as well as the management of the smart grid, the digital platform for energy transactions and analytics, and the storage of energy at district level.
A key metric in the local production of renewable energy is the annual energy output per PV panel square meter (kWh/m2). As PV panels are the sole energy production facility, this metric allows the total energy available in the district to be calculated. The energy potential of PV panels depends on solar irradiance and the PV power conversion efficiency. The solar irradiance above the earth’s atmosphere (Solar Constant) on a clear day at solar noon in the summer months is around 1380 Watts per square meter (W/m2). The earth at sea level receives about 1000 W/m2. In current PV panels, the efficiency is at the level of 20% and an equal percentage of solar radiation is converted into electricity. On an average day, a PV panel receives about 5 to 6 h of direct full sun. Thus, one square meter of PV panel will give approximately 1–1.2 kWh per day, a max of 365–438 kWh annually.
PV panels’ energy output depends on geographic location, solar irradiance and their power conversion efficiency. The NREL’s PV Watts Calculator (
https://pvwatts.nrel.gov/) (accessed 13 December 2021) allows the energy output of grid-connected photovoltaic energy systems throughout the world to be estimated. Using this calculator, we find that a PV panel system of 1 kW gives annually 1416 kWh in Athens Greece, 944 kWh in Paris France, and 894 kWh in Helsinki Finland.
The installation of photovoltaic panels on the roof of buildings can follow different patterns depending on the roof.
Figure 2 shows different types of roofs and the coverage rates for PV panels. A flat roof has a photovoltaic panel capacity of 50–70% of its surface, which drops to 40% on gable and hipped roofs.
Given the above data, we compute the energy generated by PV panels in three steps. First, we estimate the total surface of PV panels in the district, including panels on building roofs, on the ground in private yards, and in public parking spaces. We assume the maximum capacity of photovoltaic panels on roofs of buildings (70%), plus photovoltaic panels on the ground equal to 10% of residential plots, plus panels on streets and parking areas equal to 10% of this area. Second, considering the district as a Virtual Power Plant, we compute the DC system size. PV Watts Calculator proposes the following formula to estimate the system size based on the area of the array: Size (kW) = Array Area (m
2) × 1 kW/m
2 × Module Efficiency (%). For module efficiency, we consider that 1 square meter of PV panel gives 0.217 kW. This is a rather conservative estimation given that new PV panels available on the market have a power conversion capacity at the level of 30%. Third, for a given city we use the PVWatts
® Calculator to compute the energy generated annually.
This type of renewable energy can be used to reduce energy consumption for space heating (EH) and domestic water heating (EDWH). As shown in
Table 4, these categories account for 63.6% and 14.8% of household energy consumption in the EU. Currently, gas and petroleum products have high shares in the energy sources of these two energy usage categories. Air-source heat pumps (ASHP) and geothermal heat pumps (GHP) allow the transition from gas and diesel for space and water heating to electricity with a considerable energy reduction margin. There is a double gain to this; transitioning to electricity enables saving energy, using renewable energy and reducing CO
2 emissions [
59,
60].
The most common commercial solutions for heating are air-source heat pumps that take heat from the atmosphere and heat water which then circulates to heat radiators. Geothermal heat pumps are more efficient and use the heat of the earth to provide heating for houses and offices and water heating too. Compared to air source heat pumps, geothermal pumps are more energy efficient as they take advantage of ground temperatures which are more uniform than air temperatures. They can reduce energy consumption by approximately 25% to 50% compared to air-source heat pump systems [
61].
The energy efficiency of a heat pump, whether ASHP or GHP, for heating is defined by the Coefficient of Performance (COP) and for cooling by the Energy Efficiency Ratio (EER). The Total Efficiency Ratio (TER) defines both heating and cooling efficiency. A heat pump with a COP 4.0 gives 4 kW of energy by consuming 1 kW of electricity only, which corresponds to a significant energy reduction [
62].
Many publications have attempted simulations and experimental studies to assess the energy saving of heat pump-based heating systems. Zanetti et al. [
63] reviewing papers that compared different solutions of photovoltaic-assisted by air-source heat pumps show a potential energy saving of between 20–35%. The energy efficiency of geothermal heat pumps is higher; they remain an under-used technology, due mainly to the limited awareness of their potential. Their CO
2 emissions are less than half those of conventional oil boiler systems [
64]. According to Energy Saver, U.S. Department of Energy, “[a] heat pump can reduce electricity use for heating by approximately 50% compared to electric resistance heating such as furnaces and baseboard heaters” [
65].
Given this data, we can estimate the energy-saving potential of heat pumps for space heating and water heating as follows:
The concept of ‘Nature-based solutions’ (NbS) was introduced by the World Bank to underline the positive role of biodiversity on the climate. The International Union for Conservation of Nature (IUCN) defines NbS as “actions to protect, sustainably manage and restore natural or modified ecosystems, which address societal challenges (e.g., climate change, food and water security or natural disasters) effectively and adaptively, while simultaneously providing human well-being and biodiversity benefits” (Cohen-Shacham et al.) cited by [
66]. Nature-based solutions is an umbrella concept for ecosystem-based adaptation (EbA), green infrastructure (GI), and ecosystem services (ESS). These concepts are interrelated and form a dominant discourse on human-nature relationships. They are based on the same set of principles, such as multifunctionality and participation. Their differences are related to implementation in planning and practice [
67].
Nature-based solutions protect, manage, restore, or enhance natural ecosystems in cities. In the fight against climate change, NbS comprise measures that stop deforestation and increase tree canopy and green areas that capture CO
2 emissions. Prominent land-based nature-based solutions for negative emissions are afforestation, biomass for energy with carbon capture and storage, and soil carbon sequestration [
68]. Examples of NbS include trees in urban parks and forests, street trees that contribute to lowering the temperature in cities, elimination of urban heat islands, conservation of natural habitat space in floodplains, as well as architectural solutions for buildings, green roofs, wall installations for temperature reduction and energy saving through reduced cooling loads [
66].
Planting trees and expanding tree canopy in cities is the most established NbS. It is based on the capacity of trees to take CO
2 from the air and convert it into oxygen and plant material through photosynthesis. Encon, an independent agency that supports organisations to become more sustainable, having reviewed many studies on CO
2 capture by trees, estimates that “the annual CO
2 offsetting rate varies from 21.77 kg CO
2/tree to 31.5 kg CO
2/tree. To compensate 1 tonne of CO
2, 31 to 46 trees are needed. In Europe, there are 300 to 500 trees per hectare. For calculating the figures on the Encon website, we assume a rate of 24 kg CO
2/tree and an average of 500 trees per hectare. This means that 1 hectare of forest: 500 trees × 24 kg CO
2/tree = 12,000 kg of CO
2 offsets, i.e., 12 tonnes CO
2/hectare.” [
69]. However, estimations based on the US Environmental Protection Agency give a max capture capacity per 10-year urban tree at 38 lbs (17.2 kg) per year. This capacity increases substantially with tree age [
70].
In a city district, trees can be planted in three areas, (a) public gardens and green spaces, (b) on both sides of roads, and (c) in private gardens, yards, and the non-built space of plots. The maximum number of trees in the tree canopy of a city district can be estimated as follows:
- (a)
Public gardens, large and small, and city forests can contain 500 trees per hectare. We assume 60% coverage of green spaces by trees.
- (b)
Roads with trees on both sides at an average distance of 5 m from each other can contain 400 trees per km.
- (c)
Private gardens and yards may have 25% of their surface covered by trees.
Thus, the maximum capacity of CO
2 absorption by the tree canopy in a district is