CHAPTER 3

What, Who, and How to Measure

Measuring, one of the basic tools to control, manage, and improve processes, also facilitates making comparisons between alternatives to any given process. Moreover, the data generated by measurements supports planning and decision-making processes with pertinent knowledge to yield more efficient and applicable solutions. To obtain significant data that accurately portrays a given process under analysis, however, relies decisively on the task of defining what to measure and how it should be measured, a challenge to some extent in any measurement endeavor.

In general, indicators that correspond to a creation parameter or property or to a value derived therein (e.g., concentration in air of particulate matter, carbon monoxide, sulfur dioxide, or nitrogen dioxide) can be used to assess and quantify a particular situation (Wolfson 2016). Yet indicators are usually narrow and specific. Thus, indexes that integrate several indicators and that also account for the relative importance of each are also employed. A widely used example is that of air quality index, or air pollution index, which accounts for several major air pollutants. Moreover, in contrast to indicators, which are narrow and specific by definition, indexes convey more information and, in certain cases, more knowledge. In terms of sustainability measures that account for economic, social, and environmental indicators and indexes, efforts to combine multiple indicators into a single, comparative number are ongoing.

Although indexes attempt to describe given situations on the basis of the indicators they contain, not all measures are suitable to illustrate every system or process. Thus, while some measures are strong indicators and are highly sensitive in one case, in another case they provide almost no information whatsoever. We can also differentiate between the measurement of physical and nonphysical resources and tangible and intangible values. In contrast to the typically straightforward and accessible nature of physical resource measures, the measurement of nonphysical values is qualitative in some cases and quantitative in others. Qualitative assessments are used to elucidate the underlying reasons, opinions, and motivations and to provide insights vis-à-vis the problem. On the other hand, quantitative assessments are used to quantify the problem by generating data that can be transformed into usable numbers or statistics, and in so doing they quantify attitudes, opinions, behaviors, and other defined variables (Neuman and Robson 2012). Finally, all measures can be assessed at the micro or macro level. Micro-level measurements consider the various entities in the process’s supply chain, while macro-level measurements refer to the process as a whole.

Physical Resource Measures

Virtually any system or process can be assessed with physical resource measures that, when combined to create indexes, constitute a fundamental economic tool that can also be applied to environmental assessments. Physical resource measures include not only the amounts of resources extracted from the environment to run a process (i.e., inputs), but also the volume of materials discharged from the process to the surroundings (i.e., outputs). The assessment can be performed by considering the system’s inputs and outputs (i.e., by performing mass and energy balances) at both the micro and macro levels. Moreover, it is applicable to a good, a service, a process, or a city, and in many methodologies it is termed material flow analysis or resource flow analysis (Narayanaswamy et al. 2003).

When done to assess the sustainability of a system, a resource flow analysis is usually expressed in terms of the ecological, carbon, and water footprints. Ecological footprint is a measure of the area of productive land required to supply human demands and to absorb the impact of human activity on nature (Wackernagel and Rees 1998). It is calculated by summing the total area of cropland, grazing land, forest, and fishing grounds required not only to produce the energy, food, fiber, and synthetic products used by humans for housing and infrastructure as well as for leisure activities, but also to absorb the corresponding waste and polluting emissions. Carbon footprint is a measure of greenhouse gas emissions over the course of the entire value chain of a product, whether it is a good or a service, and today, it is one of the most widely used sustainability measures. Encompassing the product’s entire life cycle, the carbon footprint is based on the total energy and materials used over that life cycle, from the mining and extraction of the raw materials to the end of the product’s life (Weidema et al. 2008). Similarly, water footprint is a measure of the amount of water consumed by a process, and it includes direct water use (e.g., tap water, rivers, etc.) and indirect water use (e.g., green water, or naturally occurring ground water that also contributes to crop growth) (Aldaya 2012).

Life Cycle Assessment

One useful method for performing a resource flow analysis (i.e., input-output inventory and mass and energy balance) is life cycle assessment or analysis (LCA). A dissection of all the stages that constitute the life cycle of each good or service from “cradle to grave” (i.e., from creation to disposal) or from “cradle to cradle” (i.e., waste-free life cycle), LCA monitors the resources that were utilized and discharged at each stage and over the entire course of the life cycle of the product or service (Day 1981; Finnveden et al. 2009). As such, the negative and positive impacts on environmen can be measured and monitored. The data collected can then be applied to streamline the product life cycle, in the process reducing its negative effects relative to its positive impacts at the social and environmental levels. Similar to its use at the micro level to assess a product’s life cycle, the LCA methodology can also be applied at the macro level to the activities and services associated with entire cities.

Although there are already agreed-upon sets of criteria and procedures to define the goals of an LCA—which are to examine the inputs and outputs of materials and energy throughout the product life cycle and to define the relevant measures for decision making—the methodology also has some limitations owing primarily to the absence of nonphysical or intangible values (e.g., effort, equity, or accessibility) from its calculation. To address this shortcoming, the United Nations offered a general model, the life cycle sustainability assessment (LCSA), which offers a more holistic view of the product life cycle. Its comprehensive nature owes to the inclusion in the LCSA of the positive and negative impacts on the environmental, social, and economic perspectives of decision-making processes to guide stakeholders toward the choice of more sustainable products based on analyses that encompass the products’ entire life cycles (Finkbeiner 2010).

As such, the LCSA sums the results of the environmental life cycle assessment, the social life cycle assessment, and the life cycle costs to help clarify the trade-offs between the three pillars of sustainability (economic, social, and environmental), with the ultimate goal of helping decision makers choose the most sustainable value of each product.

Environmental Life Cycle Assessment (E-LCA)

In the years since its inception, the LCA concept has been adapted to address different aspects of the pillars of sustainability. To evaluate the potential environmental impacts of a product or a process over the entire course of its life cycle, the environmental LCA (E-LCA) was conceived. The outcome of an E-LCA can promote the design or redesign of more environmentally friendly products and the implementation of practical technological solutions (Jolliet 2015).

Similar to LCA, the E-LCA is equally applicable to the assessment of both products and services and their interaction. For example, the E-LCA of food with respect to water and energy use is a commonly used method to compare not only different products, but also different producers. The life cycles of food products, especially those of vegetables and fruit, utilize large amounts of natural resources, such as the water and nutrients directly involved in their cultivation. Furthermore, they also contribute to the emission of greenhouse gases (Yang and Campbell 2017). Perhaps the most well-known index of greenhouse gas emissions is the carbon footprint, the main contributors to which in the typical life cycle of a food product are the transportation of the food from the supplier to the consumer and, to grow the food, the provision of water through mechanized pumping systems. In addition, the carbon footprint of food also incorporates gases attributed to the use of fertilizers and to other farming-related activities associated with agriculture. (e.g., agricultural machinery, warehouse energy, etc.)

In today’s global economy, a large portion of the carbon footprint of produce is due to its transportation, which in many cases involves shipment to one or more central distribution points before the farm products make it to the market. The carbon footprint of produce can therefore be significantly reduced by obtaining fruit and vegetable products from local growers, thereby eliminating the need for transportation and reducing the amount of energy used by the conventional distribution network (Wright and Hollingshead 2011). This goal can be achieved not only by offering subsidies to local farmers, but also by encouraging farmers to sell directly to consumers or use food cooperatives. Alternatively, carbon footprint of produce can also be reduced by establishing community gardens. Not only do they yield local produce and promote a sense of community through the shared interest and participation in cultivating a garden, community gardens can also reduce the life cycle footprint of vegetables. Moreover, community gardens also add social impact to the process and effectively redesign the entire process of agricultural production as a service. In addition to fostering community identity and increasing the sense of community ownership and stewardship, community gardens also have economic benefits related mainly to food production. Furthermore, they also have environmental benefits, from beautifying the urban environment and connecting people with nature to the use of rainwater, production of oxygen, cleansing of the air, and the capture of carbon dioxide. In light of these numerous potential benefits, recent years have witnessed an increase in the number of community services departments in cities that have implemented plans to encourage their residents to create community gardens by offering assistance in their design, development, and maintenance.

To empirically determine the extent to which community gardens can reduce the carbon footprint of produce, Issac and Brown (2016) compared the LCA of conventionally grown lettuce with that of lettuce grown in community gardens and measured the carbon footprint of the produce in both scenarios. They found that the total emission of conventionally grown California lettuce 0.7 kg_eqCO₂/kg was halved to 0.35 kg_eqCO₂/kg by growing the crop in a community garden (_eqCO₂ = equivalent emission of total greenhouse gases, where the emissions of greenhouse gases other than CO₂—e.g., methane (CH₄) and nitrous oxide (N₂O)—are converted to equivalent CO₂ emission on the basis of their impact on the greenhouse effect). These promising results show that merely reducing the need for transportation of the produce effected a significant reduction in CO₂ emissions. Yet CO₂ emissions can be further reduced by implementing rainwater harvesting practices and by using water collected from air conditioners, by conserving water applied to crops via the use of ground covers, and by using compost prepared from kitchen and garden waste rather than using industrial fertilizers, whose decomposition results in the release of nitrous oxide gases, which are powerful greenhouse gases. Indeed, the resulting carbon footprint in such a scenario may even be negative, as plant photosynthesis during vegetable growth consumes CO₂.

Environmentally Extended Input-Output Analysis (EEIOA)

An environmentally extended input-output analysis (EEIOA) entails a simple and robust method for evaluating the links between economic consumption activities and environmental impacts (Kitzes 2013). While LCA is a bottom-up process-based approach that is highly specific and that accounts mainly for the direct utilization of resources, EEIOA is a top-down methodology, and its calculation also considers the impacts of indirect contributions (Skudder et al. 2016). As such, EEIOA overcomes the limitations imposed on LCA by boundary cutoff problems (Wiedmann and Barrett 2011).

The EEIOA is ideally suited to assessing the share of services (e.g., city services) in the overall input-output analysis of a process that has multiple purposes (e.g., manufacturing, agriculture, etc.). For instance, Skudder et al. (2016) recently used the EEIOA methodology to assess the environmental impact of crime in terms of its carbon footprint. The choice of this methodology to analyze the carbon footprint of crime was motivated by the researchers’ identification of an assortment of embedded emissions associated with crime. Also known as indirect emissions, they originate from the carbon associated with replacing stolen/damaged goods to that used by supporting services, such as insurance, health, legal, police, and prisons. They found that while the share of the footprint represented by stolen goods was the highest (up to 35 percent), it was followed by that of supporting services, such as health services (up to 17 percent), police activity (10 percent), prison services (8 percent), defensive expenditures (10 percent), and lastly, insurance (4 percent). Together these separate contributions to the carbon footprint constituted up to 50 percent of the greenhouse gas emissions associated with crime.

City Metabolism

To quantify material and energy flow and utilization and to control their operations and manage their services, cities around the world regularly measure a variety of indexes. Additionally, cities also perform different resource-based surveys, such as measuring the greenhouse gas emissions from different municipal sectors, to assess the impact of the city’s daily life on its citizens and surroundings. These monitoring efforts provide the data necessary to design and implement the tools and actions required to address what is perhaps the most undesirable consequence of urbanization—excessive greenhouse gas emissions associated with contemporary human lifestyles. Toward that end, the collection and analysis of the material and energy flow data of a city can facilitate more rational decision making by city stakeholders to ensure that the city remains healthy and livable for its future generations.

One of the models used to calculate city-level resource flow and to provide the notion of city sustainability with practical meaning is the metabolism model (i.e., urban metabolism). Conceptually similar to human metabolism, the city metabolism model measures the rate of resource consumption and the corresponding amounts of waste generated in a certain area (e.g., city) and translates it into carbon, water, and ecological footprints. In the frame of the metabolism model, city sustainability is assessed by measuring the total energy and materials that flow into the urban area (e.g., water, food, fuel, clothing, etc.) and the total emissions that flow out of it (e.g., air pollution, sewage, and solid waste) (Kennedy, Pincetl, and Bunje 2011).

In 2014, the city of Beer-Sheva, Israel, measured and inventoried its greenhouse gas emissions over a 13-year period from 2000 to 2012 as part of its membership obligations to ICLEI-Local Governments for Sustainability, a leading global network of more than 1,500 cities, towns, and regions committed to building a sustainable future. The public-sector data for Beer-Sheva (e.g., energy and water consumption, waste and recycling, landscaping, and more) were retrieved from municipal environmental data, while the residents’ data, for both homes and businesses, were provided by the electric and water companies. The emissions from the different city sectors in 2000, 2008, and 2012 are summarized in Table 3.1.

Table 3.1 Greenhouse gas emissions in Beer-Sheva

The results of the survey show that between 2000 and 2012, overall city emissions increased by 27 percent, a rate similar to that of the city’s population growth during the same period. Additionally, the findings show that emissions connected with the domestic and commercial sectors not only increased steadily during that period, they consistently accounted for more than half of the city’s total emissions. The municipal sector, on the other hand, was found to be directly responsible for only about 5 percent of overall city emissions, the main contributing sources to which were water and sanitation, street lighting, and public buildings (Table 3.2). The data also show that the relative share of emissions from the waste sector decreased by more than 40 percent between 2000 and 2012, a finding that is a direct result of the city’s implementation of a new waste recycling program. By diverting city waste from the landfills, this program effectively reduced greenhouse gas emissions per ton of treated waste. However, the concomitant doubling of the level of trade in Beer-Sheva during the same period resulted in a 80 percent increase in greenhouse gas emissions share associated with the commercial sector, which includes retail stores and offices.

Table 3.2 Greenhouse gas emissions for the municipal sector in Beer-Sheva

Table 3.3 Examples of projects that contributed to reduction in Beer-Sheva’s greenhouse gas emissions

*Based on 10 years in total
**$25 per kg eqCO₂

Finally, to further reduce its emissions, the city also invested in technologies. One such technology, the computerization of the municipal irrigation system, led to an immediate and significant reduction in municipal water consumption. Insofar as supplying a city’s water needs relies on energy—to desalinate sea water or to pump water from wells and to transfer the water to its point of use—the computerization of the city’s irrigation system reduced the greenhouse gas emissions associated with these activities (Table 3.3). For these currently minor achievements to have significant and long-term impacts, however, the goal of reducing greenhouse gas emissions must include both domestic and commercial sectors. The main challenge, therefore, is to instill in every house and business the common desire to cut emissions. To that end, the municipality is ideally situated to disseminate the message by implementing different services, from education programs and advertising to dedicated programs that help retail businesses make their operations greener.

Based on the survey and on the city’s ability to design, manage, and invest in projects that could contribute to the reduction of its greenhouse gas emissions, a list of potential projects was suggested (Table 3.4). The final decisions regarding which projects would be included in the program were taken using a prioritization model built in the framework of the city’s master plan for the reduction of its carbon footprint. To that end, each project was assessed on the basis of five criteria: (1) costs and resources, (2) reduction potential, (3) the potential to implement the project immediately, (4) the complexity of the process in terms of the participating entities, and (5) degree of visibility and impact of the project on changes in the city. Each project was then prioritized on a scale of 1 (low priority) to 5 (high priority).

Table 3.4 Prioritization of proposed projects to reduce greenhouse gases in Beer-Sheva

Prioritization analysis

Nonphysical Resource Measures

The quantification of the nonphysical resources—such as time, effort, or knowledge—involved in a process or service and along the value chain is much more difficult. Insofar as services provide intangible value, the nonphysical measure assessed for a service is performance, which is evaluated in terms of productivity, efficiency, and quality. Stated differently, a service’s performance is reflected in its organization, level of customer satisfaction, the extent to which mutual trust develops between provider and customer, and the profits generated by the service (Rust and Oliver 1994; Johnston and Jones 2004; Wolfson et al. 2015).

A time assessment, a classic example of nonphysical resource quantification, is a potentially valuable tool whose resultant data can be practically applied toward improving the process and increasing customer satisfaction as well as the service’s sustainability. In addition, time assessments can yield information that can promote the efficient allocation of the provider’s resources, from the amount of manpower required to complete a given assignment to the qualifications of the employees doing the work, the amount of computing time needed, and the choice of computer software. Indeed, the mismanagement of these and other provider resources can lead to higher overall costs to the provider (i.e., life cycle costs).

The assessment of another nonphysical resource, effort, that was invested by the provider and the customer in the cocreation of a service, can also help identify more efficient routes to perform the service, and thus to increase the sustainability of a solution. For instance, the decision to participate in organized carpooling (i.e., more passengers per journey) instead of driving alone will require greater effort on the part of all stakeholders—the driver and passengers—to coordinate the logistical details. However, assuming that each carpool passenger represents one less car on the road, their carpooling efforts will also reduce their respective environmental footprint, as the resources that were allocated for the journey before carpooling (i.e., more cars making the same trip) are now split between all the passengers traveling in a single vehicle.

Another important nonphysical measure for evaluating services is the level of the value created by the service, and in general, the higher that value, the better the service. The quality of the value can be assessed by using the data/information/knowledge/intelligence/wisdom (DIKIW) pyramid or hierarchy, which ranks the overall value of a service in terms of where it is situated on the DIKIW pyramid (Ackoff 1989). In this respect, the creation of wise values begins with the collection of raw data that is communicated and then processed into information that can eventually be utilized to inform decision making and generate more knowledge. This knowledge can then be invested in intelligent solutions that increase process efficiency and that ultimately generate wisdom that increases the effectiveness of the value. In short, a wise value is that which strikes a balance between individual and collective human values while having a positive impact on future generations (Spohrer et al. 2016; Wolfson 2016).

In terms of the sustainability of a city, municipal services create a range of values that can be assessed using the DIKIW hierarchy. For example, reports about the number of complaints citizens submitted to the city by telephone or e-mail or the amount of garbage that was collected per month constitute typical crude municipal data. Insofar as such crude data typically provide only a general picture of what happens in the city, they usually are insufficient by themselves to form a basis for action. Yet the city can publish information about city events, such as the type, time, and place of the event, which allows both the city’s inhabitants and its visitors to not only know what is happening in the city, but also enables them to plan their schedules and activities accordingly, regardless of whether they choose to participate in the event. Likewise, municipal services also provide citizens with knowledge that, as a value, allows them to reap more benefits from the service, thereby performing more complex action—for instance, giving citizens insight and assistance regarding the city’s laws and procedures while he submit a building program. With respect to intelligence as a value, it should be an integration of data, information, and knowledge from different fields. It could be contained, for example, in the preparation of a master plan in a certain field or of a municipal budget. However, strategic plans must consider myriad future requirements that transcend current needs and wants while integrating numerous factors and including uncertainties and forecasting to yield a smart value.

Finally, assessments of the level of the value of every service, and even of the building blocks of each service’s value chain, can increase the value and improve the service, ultimately to achieve higher customer satisfaction. For example, a call center or comparable Internet-based platform that allows citizens or other stakeholders to submit complaints and queries is usually situated at the level of data or, at the most, information. However, the inclusion of a built-in questionnaire may enable the service to collect more accurate and valuable information that may even qualify as knowledge. For example, a citizen report about a potentially dangerous event in the city (e.g., flooded bridge, traffic accident) can provide data about the event’s location and time (i.e., information), but it could also include personal details about the person who submitted the report and whether this was an isolated event or something that occurs periodically (i.e., knowledge). Moreover, more intelligent value can be gained from the report submittal service by rapidly addressing the issue in the report (i.e., the dangerous event) and then reporting back to the complainant about what was done, which would also encourage their continued use of the service. The extent to which the municipality responds in a timely manner, as well as the effectivity of its response, is a significant contributor to the value of the service. Furthermore, the integration of the data and information obtained from the complaint and from the actions that were taken and their subsequent incorporation in municipal action plans can increase the level of the value to wisdom.

Another service offered in many cities today that can be analyzed in terms of its value and the extent to which it promotes city sustainability is that of recycling. The data and information about the placement and emptying times of collection bins and about the average amounts of recycled material collected over a given time period (e.g., per day/month/year) can be combined with information about the recyclable materials. Thus, information about the path taken by the recyclable materials, from their production in a factory to the city where they are consumed to the recycling plant, and about the subsequent uses of the recycled product can be illustrated on the product container, on the city’s collection bins, and/or on the municipal website. The collection and processing of such data provides stakeholders with important knowledge about the manufacturing process from which the product is obtained and the consequences of this process. Applied to the example of a plastic bottle, the intelligent knowledge provided vis-à-vis the city’s recycling endeavor can be leveraged—via supporting services such as advertising and educational programs—to motivate people to make smarter choices when shopping in the supermarket. In the long term, as more people become aware of the deleterious effects of plastic on their environment, this particular combination of services could lead to lower plastic bottle production levels and the accumulation of correspondingly less waste.

City Performance Measures

All of the cities worldwide measure different indicators and indexes. Recently, the International Organization for Standardization (ISO) issued standards—named indicators for city services and quality of life (ISO 2014)—for the sustainable development of communities. Representative examples of these indicators are listed in Table 3.5.

Table 3.5 Representative city performance measures

The indicators in Table 3.5 are designed not only to keep the public informed by providing metrics data related to long-term community legacy goals, but also to promote the city’s goals and objectives. In addition, these measures can be used as objective tools, specific to each city department, to evaluate the quality and efficiency of work.

Most cities also measure success parameters via assessments of the efficiency of their performance or of the residents’ levels of satisfaction with city services, which is usually done through dedicated questionnaires and surveys that measure performance (Andreassen 1994; Kantorová and Růžička 2015). Done regularly, such assessments can indicate how effectively and efficiently the city delivers its services, and the knowledge the city obtains can then be applied to continuously improve those services and to guide the city in its subsequent planning efforts. For example, the city of Novato, California, surveyed its residents for their opinions about the community and about the quality of the services provided by the local government. A representative sample of 3,000 households was surveyed with a 28 percent response rate and a 3 percent margin of error (City of Novato 2013). Table 3.6 illustrates some representative results in different fields. To ensure that such assessments provide reliable and useful data, however, the assessment process itself must be evaluated. For example, police department of the city of Dallas submitted an audit of its performance measurement process that included five selected performance measures. The audit showed that results reported for only two of the five performance measures were reliable (City of Dallas 2016).

Table 3.6 Representative results of city of Novato citizen survey

* Comparison to California cities with populations of 32,000 to 65,000.

Social Life Cycle Assessment (S-LCA)

An S-LCA comprises a set of methods used to assess the potential or real social impacts of a product or service. The term “social impacts” as used here refers to human capital, human well-being, cultural heritage, and social behavior (Jørgensen 2008; Muthu 2015). The overall goal of performing an S-LCA is to improve the social performance of products at different stages of their life cycles while considering the relevant stakeholders in their entirety (e.g., workers, customers, local community, and the city) (Wu, Yang, and Chen 2014). An S-LCA can be applied via two main routes: (1) by focusing on a performance reference point, such as workers’ living and working conditions (e.g., human rights, labor conditions, and discrimination), and (2) by focusing on impact pathways, which here relate to humans and their biological living conditions (e.g., health and safety).

For example, solid-waste management services in Kathmandu, Nepal, were assessed with an S-LCA (Gautam 2011). The stakeholders of the evaluation were identified as workers, the local community, society, and all actors in the value chain. Representative indicators that were assessed during the study and the main findings are illustrated in Table 3.7.

Table 3.7 S-LCA of solid-waste management services in Kathmandu, Nepal (Gautam 2011)

Sustainable Service Assessment

To ensure the sustainability of a service in general and that of a city’s agglomeration of services in particular, each service should be considered in terms of its environmental, social, and economic impacts via a sustainable service assessment. Moreover, the service should be assessed in terms of all activities and inputs and outputs associated with the service (i.e., life cycle assessment), and its value chain should be illustrated in terms of all the physical and nonphysical resources utilized in each building block of the chain. The resources should then be quantified—if possible, for each link in the value chain, but if not, then for the entire value chain—to promote improved resource allocation and to design more efficient and effective services. In addition, as the production and delivery of a service necessitate the involvement of both the provider and the customer, the division in resources between the two should also be presented. Likewise, the distribution between the resources associated with the core-value (CV) of the service (i.e., the essence of the solution that a certain service provides) and those associated with the super-value (SV) (i.e., other supporting and complementary values) should also be considered. Finally, as the same service can be provided via different modes (e.g., electronic service, self-service, etc.), LCA should be done for each mode in a manner that facilitates their comparison to enable people to choose intelligently between them.

To illustrate a sustainable service assessment, the payment process for municipal services was chosen. The assessment was done in terms of direct physical resources (e.g., water, energy), facilities (e.g., office, computer), effort (e.g., time and manpower), and the value according to the DIKIW pyramid (Ackoff 1989).

Cities provide different methods of payment for municipal services such as education, document issuing, and refuse collection. Traditionally, municipal services were paid for in the person-to-person (P2P) mode in the relevant city office, at the bank, or at the post office. Today such payments can be made via a phone call, on the city’s website, or by using different smartphone applications. For example, most cities today offer an e-service that allows customers to remotely pay for municipal services online instead of driving to the municipal office or the bank to make the transaction through a teller. Table 3.8 illustrates the division of resources and capabilities between provider and customer in the P2P and e-service modes in terms of the service’s CV and SV.

Table 3.8 Comparison of the division of resources and capabilities between customer and provider in person-to-person (P2P) and e-service payment modes

¹ On the basis of carbon footprint measurements for various transportation means per passengerkilometer-traveled (Chester and Arpad 2009) and carbon footprint measurement per employee by HomeStreet Bank (Seattle Climate Partnership 2009).
² On the basis of transaction times of 10 minutes (Google Environmental Report 2017).

Both payment scenarios could be made more sustainable by using more efficient technologies—for example, by using electricity that is based on renewable energy (e.g., solar or wind power) or by using conventionally produced electricity more efficiently (e.g., more efficient computers). Not only can these solutions save money, they can also result in the generation of less pollution. But, as illustrated in Table 3.8, shifting from a P2P service to an e-service affects all aspects of the service in terms of both the CV and the SV provided by each service. Regarding physical resources and facilities, while the P2P mode requires a building and an office, which require a multitude of resources for their daily operation, from water and electricity to paper, computers, and furniture, the e-service mode requires neither an office nor a building, and it eliminates the need for the customer’s transportation to and from the office. Moreover, the e-service also requires less manpower. Yet the shift to a reliance on e-services requires greater customer involvement in terms of resources and capabilities, as the customers, who perform the bulk of the activity, must use their own facilities (e.g., home, computer, and Internet line) and invest their effort and knowledge. However, the time that the customer must invest and the total energy used per transaction are also lower in the e-service mode. Furthermore, the physical resources associated with each service’s SV are tremendously different between the two modes. For one, making the shift to the e-services mode renders the office and the corresponding resources required for its operation (but that are not directly needed or involved in the payment itself) redundant. This finding is in agreement with similar results showing that a significant reduction in resources can be achieved by shifting from the super-service mode (i.e., service is operated mainly by the provider) to the self-service mode (i.e., service is operated mainly by the customer) of a given service (Wolfson, Tavor, and Mark 2012).

The next step of the assessment should be the division of resources between the various stages of the life cycle. The value chains of the P2P and e-service service modes are illustrated in Figure 3.1. Two representative analyses were performed to compare the two scenarios: (1) physical resources in terms of greenhouse gas emissions and (2) nonphysical resources in terms of time.

The total greenhouse gas emissions of the P2P mode of a bill payment service comprised two main components: the energy used (by both provider and customer) to travel to and from the municipal office or bank and the energy used by the provider to maintain the office. The greenhouse gas emissions of a journey depend on the type of vehicle and on the roundtrip distance traveled to the municipal office or bank. Chester and Arpad (2009) performed an environmental assessment of passenger transportation based on energy use or greenhouse gas emissions per passenger-kilometer-traveled (PKT). The survey included operational components that can be assigned to the service’s CV (e.g., running the vehicle and idling time) and nonoperational components that can be assigned to the service’s SV (e.g., vehicle manufacture, maintenance and insurance, infrastructure construction, operation and maintenance, and fuel production). They found emissions of 51 gCO₂ PKT in the case of an urban diesel bus at peak hours and 234 gCO₂ PKT for a car with one passenger. Thus, as expected, using public transport in the P2P service mode is much more sustainable than using private transport. Considering a round-trip journey of 6 km, the total emission of the customer is between 306 and 1,404 gCO₂. To this should be added the emissions of the provider. HomeStreet Bank calculated greenhouse gas emissions per employee for a year (Seattle Climate Partnership 2009), assuming 25 working days per month and 10-hour workdays and considering employee commuting, energy use, waste generation, and so on. Based on this calculation and a scenario of 5 minutes for each transaction, the total greenhouse gas emission is 115 gCO₂. Hence, the total emission of the P2P scenario is between 421 and 1,519 gCO₂ and is attributed mainly to the customer.

Figure 3.1 Value chain of payment for municipal service: (a) P2P, (b) e-service

In contrast, greenhouse gas emissions of the e-service scenario are attributed mainly to the energy used to power the computers of the customers and those of the provider, including the provider’s server farm. In this case, the calculation was based on annual CO₂ emissions of 300 million tons generated by 1.9 billion Internet users for 365 days per year. Each payment transaction was estimated at 10 minutes, as the customer usually needs more time than the teller to complete the transaction, for a total time of 10 hours of computer use. Based on this calculation, the customer’s emission is 7.3 gCO₂ while that of the provider is due mainly to the energy used in running the server farm. To assess the emissions of a typical server farm, Google performed a greenhouse gas emissions analysis. Depending on the source of the energy used to power the server farm, its emission amounted to 0.2 to 0.5 gCO₂ for each Internet search of a duration of several seconds (Google environmental report 2017). Therefore, the server emission generated in a 10-minute transaction is estimated at 120 to 300 gCO₂. Taken together with the emission assigned to the customer, the total emission for a single payment transaction is 127 to 307 gCO₂, which is attributed mainly to the provider. This greenhouse gas emissions analysis shows clearly that exchanging the P2P service mode with an e-service payment system results in a significant reduction in the associated emissions.

A time assessment of the two scenarios shows that the payment process is much more efficient in the e-service mode, which can be performed at any time of day and from any location. Use of the P2P mode requires the customer to drive to and from the office, and total travel time can be estimated at 12.2 minutes (6 km roundtrip at 50 km per hour with 5 minutes allotted for parking). Add to that an average waiting time of 15 minutes before the customer is served and 5 minutes for making the actual payment through the teller, and the total time needed to pay a municipal bill in the P2P payment mode is 32.2 minutes. Performed in the e-service mode, in contrast, the whole process is estimated to take about 10 minutes. Moreover, because use of the e-service requires greater involvement in and responsibility for the process on the part of the customer, he or she can further streamline the payment process for municipal bills by making several payments each time he or she goes online to pay municipal bills. Likewise, customers can even manage their payments to the city more efficiently by, for example, making all their payments through a single application that, in addition to keeping a central record of all customer payments, can alert the customer in the event that an unusual payment is made, thereby conferring on the customer greater control over the payment process. Taken together, the many benefits of using the e-service increase the level of the value generated by the service.

Although the e-service mode is more accessible, viable, and effective in comparison with the P2P service mode, the latter should not be eliminated entirely. Because the technological solution may not be used or easily accessible by all of the citizens (e.g., the elderly, people without a computer), the municipality should continue to offer the P2P mode as a payment option. However, that could be done using a less intensive format—for example, by reducing the number of hours the payments office is open for business.

Finally, to further increase process efficiency and effectivity, its value chain can be assessed in terms of the DIKIW value hierarchy. An examination of the value chain of an e-payment system to enable citizens to pay for municipal services (Figure 3.1b) shows that the first step, the payment request, is at the level of data, the second and third steps require more knowledge, and the final step, processing, is more at the level of intelligence. Yet the location of the service as a whole in the DIKIW hierarchy can be improved through a variety of means. For instance, the first step may include information about the payment process, the essence of the payment, and how it can be reduced, which will supply the service’s customers with more information or knowledge that they can then employ toward using the municipal service more efficiently in the future. Furthermore, extra steps can be added to the payment process after the final step of processing. For example, the municipality could also include an optional questionnaire to obtain customer feedback about the process that can be practically applied to improve the process itself, thus yielding a more intelligent or wise value for subsequent customers who use the e-payment service.