What part of the economy transforms raw materials into manufactured goods?

1.3 Hydropower and electricity consumption of Nepal

The Gross value added (GVA) of electricity, gas, and water group under secondary sector in Nepal is estimated to grow by 12.97% in the current fiscal year. Such growth rate of this group had remained negative by 7.40% in FY 2016. The contribution of this group to GDP that stood at 1.02 last year is estimated to reach 1.16% this year with 0.14% point growth.

The access of total population to electricity stands at 74%. GVA and contribution of the group are estimated to remain high in the current fiscal year as a result of significant level of electricity generation in this year compared to that of the previous fiscal year. An additional 105.3 MW of electricity has been generated in the first 8 months of current fiscal year in contrast to merely 18.5% electricity production in the same period of previous fiscal year (Ministry of Finance, 2017).

The total electricity production that had stood at 855.89 MW by the end of FY 2016 grew by 105.3 MW (12.3%) in the first 8 months of the current fiscal year and reached 961.2 MW. The total electricity generation in Nepal has reached 965.7 MW (production at full capacity) with the inclusion of 4.5 MW electricity generated through rural electrification projects that are not associated with National Integrated Power System, while the electricity consumption including all sectors of economy totaled 3043.35 GWh in the first 8 months of current fiscal year 2017. Such consumption stood at 3718.97 GWh in previous fiscal year 2016 (Ministry of Finance, 2017).

While analyzing the consumption trend from FY 2011/12 until mid-March of FY 2017, private household tops the electricity consumption table with an average consumption of 45.3% followed by industrial estate with 35.6% and other sectors with 11.2% and trade with 7.9% (Ministry of Finance, 2017).

The electricity consumption of household, industry, trade, and other sectors stood at 48.2%, 32.4% 7.7%, and 11.7%, respectively, in FY 2016, while such consumption figures are 45.4%, 36.0%, 7.4%, and 11.2%, respectively, in the first 8 months of the current fiscal year. Industrial sector had shared the most in the total electricity consumption in FY 2005, while household sector has been leaving all sectors behind in terms of such consumption in the years thereafter (Ministry of Finance, 2017).

Nepal's “national energy crisis” is partly caused by inconsistent hydropower whereby climate change will likely exacerbate. Nepal's river flows vary by season. The South–west monsoon delivers roughly 80% of Nepal's rainfall between June and September. In these months, the hydro plants installed are nearly sufficient to meet demand. In the dry season, however, Nepal's reduced supply falls far short of peak load. Most of existing hydro plants in Nepal are run-of-the-river type and lack reservoirs to enable storage (ADB, 2015a,b). As a result, NEA has been forced to impose widespread load shedding during the dry winter months, particularly in the evening hours when demand is highest. Reservoir-type hydro plants could help Nepal overcome this challenge (ADB, 2015a,b). However, these come with other social and environmental considerations.

Nepal declared a national energy crisis in 2008 after a flood of the Koshi River destroyed a key transmission line importing electricity from India, and drought in another part of the country reduced supply (World Bank, 2011). Similar extreme events, and the inconsistency of Nepal's hydro resources, may be exacerbated by climate change. While the impacts remain uncertain, likely effects include changes to patterns of precipitation and glacial retreat.

Projections show that Nepal's runoff could decline by as much as 14% due to climate change, reducing the generation capacity of existing plants and the economic feasibility of new ones (Pathak, 2010). Though there has been expansion in all service sectors, it could not be made reliable. The import of petroleum products declined drastically due to the border obstruction from India in 2016 right after the year when Nepal had a massive earthquake. India is by far the largest and most significant trade partner for Nepal as it accounts for 90% of the trade. In the fiscal year of 2017, import registered significant growth due to reopening of border and supply ease.

By first 8 months of the current fiscal year, transmission line has been extended to 3204 circuit kilometers against 2848.9 circuit kilometer extended in the same period previous year. The number of electricity consumers that stood at 2,969,576 last year grew by 5.1% and reached 3,121,902 by the first 8 months of the current fiscal year (NEA, 2017).

35.3.1 Economic Impact of Droughts

Droughts are considered to be slow-onset disasters that have a direct impact on the agricultural sector and those subsectors within the secondary sector which uses and employs heavily water-dependent production processes. There is a large volume of scientific studies confirming that scarcity of water or drought-like conditions negatively affect the agricultural sector as they directly affect production, productivity, and farm practices. Desai (2003) observed that in an agriculture dominant economy, droughts affect the supply of food and increase the production risk of cotton, affecting the textile industry. Therefore, due to droughts, the agro-based industries suffer that further reduces the agricultural value-addition. Droughts lead to not only difficulties in subsistence agriculture but may intensify health risks due to water stress; the agricultural wages are impacted which cause a reduction in demand for labor and therefore the economic output declines especially in labor-intensive economies (Hayati, Yazdanpanah, & Karbalaee, 2010; Mueller & Osgood, 2009; Singh, Feroze, & Ray, 2013; Udmale et al., 2015). Sen (1982) argues that due to inflation induced by crop losses, poor farmers become poorer by higher spending on procurement of food grains at inflated prices and are often forced to sell their productive assets (such as livestock, gold/silver, or land) to meet their livelihood challenges. This adversely affects the agrarian economy and aggregate rate of growth, especially in the absence of viable and alternative livelihood options (McPeak & Barrett, 2001). Droughts may also trigger migration of labor (Dallmann & Millock, 2017; Gray & Mueller, 2012; Murali & Afifi, 2014) which may further limit availability of labor and cause productivity losses due to the decreased supply of labor at the farm level in subsequent years. Further, severe droughts may reduce the profits and capacity of farmers to invest in advanced techniques which in turn causes a reduction in agricultural growth and thereby hampers aggregate growth (Sheng & Xu, 2019).

In the disaster literature the empirical relationship between droughts and economic growth (both agricultural and aggregate GDP growth) is mostly examined through cross-country analysis. For example, using panel autoregressive distributed lags model, Raddatz (2009) confirms that among climatic disasters, droughts hurt economic growth the most causing a reduction of about 1% in the GDP per capita. Loayza, Olaberria, Rigolini, and Christiaensen (2012) in a study considering a sample of 68 developing and 26 developed economies show that droughts lead to an adverse impact on agricultural growth, although the impact on GDP growth is observed to be negative, it is weak and statistically nonsignificant. However, for developing economies, droughts have had a statistically significant negative impact across all economic sectors and the growth rate of aggregate GDP. Fomby, Ikeda, and Loayza (2013) who analyzed a sample of 84 countries (including 60 developing countries) also reported similar findings with regard to droughts especially for the sample of developing countries. Felbermayr and Gröschl (2014) use a physical intensity variable from their own GeoMet data over the period 1979–2010 and observed that droughts negatively impact high-income economies rather than low-income economies. The results differ significantly from earlier studies as it also demonstrates that the level of development (determined by per capita income) is negatively correlated with the impact of disaster. In a recent attempt to examine the relationship between natural disasters and economic growth, Panwar and Sen (2019a) find that drought, along with other disasters, affect agricultural growth negatively especially in developing countries, while severe droughts have widespread economic consequences and may lower the aggregate GDP growth in developing countries by 3%.

There is limited systematic evidence of the growth effects of droughts at the subnational or regional level. Most of the studies are event-specific and there are differences in the estimated results for the same event. However, few of the important studies in this regard are discussed here. For instance, Diersen, Taylor, and May (2002) examine the direct economic impacts of the 2002 drought on crops and livestock and related “secondary effects” in South Dakota, United States. The study reports drought-induced losses of around $1.8 billion, while for the same event, Diersen and Taylor (2003) estimate USD 1.4 billion losses after adjusting the federal aid of USD 100 million to the state. Similarly, Horridge, Madden, and Wittwer (2005) observe 1.6% reduction in Australian GDP (1% reduction is due to losses in the agriculture sector and remaining 0.6% is owing to secondary effects on the economy) using computable general equilibrium model for 2002–03 drought. Kulshreshtha, Grant, Marleau, and Guenther (2003) estimate that the economic costs of the 2001 and 2002 droughts at the regional level in Canada were approximately USD 2.34 billion. In a recent study, Howitt, MacEwan, Medellín-Azuara, Lund, and Sumner (2015) find that the overall economic impact of the 2015 California drought may be as high as USD 2.74 billion which could also result in 21,000 job losses. Few studies examined the impact of rainfall variations as a proxy of droughts but the findings of these studies are also limited to an event or a region. For instance, Dercon (2004) examined the impact of reduced rainfall in Ethiopia and revealed that a 10% lower rainfall with 4–5 years’ lag could have reduced the current economic growth of the country by at least 1%. Gadgil and Gadgil (2006) analyzed rainfall variations and their impact on the GDP growth rate to show that the negative 25%, 20%, and 15% variations cause a reduction in GDP by 7.04%, 5.13%, and 3.47%, respectively.

This review of the existing literature indicates that there are limited studies on the relationship between droughts and economic growth especially at the subnational level. As such this present study focuses on state-level data in India with an aim to fill the gap in the literature.

The importance of water use in the primary sector

Usually, economic activities are categorized into three different sectors. The primary sector of the economy, the sector that extracts or harvests products from the Earth, has the largest WF on Earth. This sector includes activities like agriculture, forestry, fishing, aquaculture, mining, and quarrying. The green WF of humanity is nearly entirely concentrated within the primary sector. It has been estimated that approximately 92% of the blue WF of humanity is just in agriculture alone (Table 7.1).

Table 7.1. Global WF within Different Water-Using Categories during 1996–2005

Economic SectorWater Use CategoryGlobal WF (109 m3/year)RemarkGreenBlueGrayTotal%Primary sectorCrop farming5,7718997337,40481.5Pasture913––91310.0Animal farming–46–460.5Water for drinking and cleaningAgriculture total6,6849457338,36392.0Aquaculture?????No global dataForestry?????No global dataMining, quarrying?????No global dataSecondary sectorIndustry (self-supply)–383634004.4Water use in manufacturing, electricity supply, and constructionMunicipal water supply–422823243.6Water supply to consumers and (small) users in primary, secondary, and tertiary sectorsTertiary sectorSelf-supply?????No global dataConsumersSelf-supply?????No global dataTotal6,6841,0251,3789,087100

Note that the blue WF figure for crop farming relates to evapotranspiration of irrigation water at field level; it excludes losses from storage reservoirs and irrigation canals. The blue WF figure for “industry” presented here includes water use in mining, which is part of the primary sector. The figure excludes water lost from reservoirs for hydroelectric generation. All gray WF figures are conservative estimates. Forestry is not included as a water use sector because of a lack of data.

From Mekonnen and Hoekstra (2011) for crop farming; Mekonnen and Hoekstra (2012) for pasture and animal farming; Hoekstra and Mekonnen (2012) for industry and municipal water supply.

The secondary sector covers the manufacturing of goods in the economy, including the processing of materials produced by the primary sector. It also includes construction and the public utility industries of electricity, gas, and water. Sometimes, the public utility industries are also mentioned under the tertiary (service) sector, because they not only produce something (electricity, gas, purified water) but also supply it to customers (as a service). Water utilities could even partly fall under the primary sector, because part of the activity is the abstraction of water from the environment (rivers, lakes, and groundwater). The work of water utilities comprises water collection, purification, distribution and supply, wastewater collection (sewerage), wastewater treatment, materials recovery, and wastewater disposal. It is rather common to categorize the whole water utility sector under the secondary sector. The tertiary sector is the service industry and covers services to both businesses and final consumers. This sector includes activities like retail and wholesale sales, transportation and distribution, entertainment, restaurants, clerical services, media, tourism, insurance, banking, health care, defense, and law. Even though sometimes categorized into another quaternary sector, one can also list activities related to government, culture, libraries, scientific research, education, and information technology. The secondary and tertiary sectors have much smaller WFs than the primary sector.

It is difficult to get water use statistics organized along the same structure of economic sector classifications. Many countries and regions have their own classification of economic activities, distinguishing main sectors and subsectors. One of the international standard classifications is the Industrial Classification of All Economic Activities of the United Nations (UN, 2008). Conventional water use statistics mostly show gross blue water withdrawals and distinguish three main categories: agricultural, industrial, and municipal water use (FAO, 2014). WF statistics also distinguish between the agricultural, industrial, and municipal sector. These three sectors cannot be mapped one-to-one onto the primary, secondary, and tertiary sector. “Agricultural water use” obviously is about water use in the primary sector, whereas “industrial water use” is about water use in the secondary sector. However, water use in mining—part of the primary sector—will generally be categorized under “industrial water use” as well. Industrial water use refers to self-supplied industries not connected to the public distribution network. It includes water for the cooling of thermoelectric plants, but it does not include hydropower (which is often left out of the water use accounts altogether). Municipal water use—often alternatively called domestic water use or public water supply—refers to the water use by water utilities and distributed through the public water distribution network. Water utilities provide water directly to consumers, but also to water users in the primary, secondary, and tertiary sector.

The mismatch between the three main categories in water use statistics and the different sectors as usually distinguished in the economy can be quite confusing. The “water supply sector” as distinguished in economic classifications refers to water utilities delivering municipal water to households and others connected to the public water supply system. Unfortunately, the category of municipal water use lumps water use for a great variety of water users: final consumers (households) and users in all economic sectors. Specifications by type of user are not always available. Additionally confusing is that even though the “water supply sector” serves all types of users, the sector refers to only a minor fraction of total water use. Most of the water use in agriculture, the largest water user, is not part of the “water supply sector.” Furthermore, water self-supply by industries does not fall within this sector, and neither does self-supply in the tertiary sector or self-supply by final consumers. Given that only an estimated 3.6% of the total WF of humanity relates to what we call the “water supply sector” (Hoekstra and Mekonnen, 2012), the sector receives disproportionate attention in public debates about water use and scarcity, diverting the necessary attention to water use in agriculture and industry.

An additional problem is that the contribution of agriculture to water scarcity is underestimated by conventional water use statistics, which show gross blue water abstractions. In agriculture, most of the gross water use will evaporate from storage reservoirs, irrigations canals, or from the field. The water abstracted for irrigation in agriculture is thus largely unavailable for reuse within the basin. In industrial water use, the ratio of net to gross abstraction is estimated at less than 5%. In municipal water use, this ratio varies from 5% to 15% in urban areas and from 10% to 50% in rural areas (FAO, 2014). Water that returns to the catchment after use can be reused. Presenting gross or net water abstractions thus makes a huge difference for industries and households and less of a difference in agriculture.

Even though the primary sector is the largest water user, governmental programs to create public awareness of water scarcity often focus on public campaigns calling for water-saving at home. This is not very effective at large given the fact that the major share of water use in most places relates to agriculture and, secondarily, to industry. Water scarcity is thus generally caused mostly by excessive water use in agriculture. Installing water-saving showerheads and dual-flush toilets in households will have barely any impact on mitigating water scarcity, but still this is what most water-saving campaigns advocate. It would be more useful to make people aware of the water use and pollution underlying the food items and other products they buy and to advocate product labels that show the sustainability of the WF of a product.

Manufacturing as an Economic and Technical Process

There is a long established division of production into three sectors by both economists and technologists (Fisher, 1939). The primary sector involves the extraction of raw materials from the Earth. This extraction results in raw materials (e.g., coal, iron, copper, wood) and basic foods (e.g., corn, wheat, soya, and meat). The secondary sector involves the transformation of raw materials into goods. This transformation results in very wide range of products that both modify primary material properties and integrate different raw materials into complex material assemblies. Processed foods represent one output of secondary production. Manufactured goods range in complexity from simple shaping (e.g., production of containers or furniture) to transportation (e.g., automobiles or trains) and electronics (e.g., personal computers or smartphones). The tertiary sector delivers services to individuals, segments of society, and businesses. Restaurants, for example, represent one way in which agricultural wealth is distributed to individuals whereas technical sales help to get manufactured goods to those who can profitably use them (e.g., construction equipment to companies or kitchen appliances to families). In recent decades, as information technology has risen in importance, extensions of this classification system have been offered that are often focused on refinement of the tertiary sector (e.g., Masuda, 1980; Karvalics, 2007). The classical analysis (Clark, 1957) suggests a regional or national economy develops as it moves from a reliance on primary production to increasingly important secondary and tertiary sectors.

The term manufacturing is generally understood to refer to secondary production. It can be considered either from a technical point or from an economic one. A sequence of unit operations operated in series, or simultaneously, can effect changes in the shape, properties, and surface conditions that are then reflected in characteristics such as fit, function, load carrying capability, and durability, etc. In the alternate framework, manufacturing is considered to add value to a raw material, or set of raw materials, such that the manufacturer can realize a net profit through the sale of the resultant article or device. Generally, it is presumed that primary production requires high capital and provides thinner margins and stronger economies have an increasing preponderance of secondary and tertiary production. Of course, the sectors described form a system and cannot function independently. A rise in the demand for manufactured goods places increased demand on the primary production of raw materials. Environmental burden and sustainability can be benchmarked, therefore, through an analysis of raw material production.

32.3 Scenario setting

In this section, the key parameters in the modeling analysis for the two scenarios are presented. The assumptions relating to the economy and population remained the same for the two scenarios. Other parameters have been documented in references [4,9,11,19]. The population and urbanization assumptions were documented in [3] and given in Table 32.1.

The GDP growth used here was revised and based on previous IPAC studies and recent trends in China. The GDP growth and structure changes were calculated based on the IPAC-SGM model, which is a computable general equilibrium (CGE) model. Fig. 32.2 presents the GDP assumptions used in IPAC model and Fig. 32.3 present the structure changes in the secondary sector [19] (Zhou et al. 2017).

The share of GDP from energy intensive industries (middle part in Fig. 32.3), in future reduce due to a change in demand. It is predicted that China's GDP will surpass that of the United States between 2020 and 2030 as such a large GDP could not in future rely on existing economic patterns which are driven by heavy industrial development and raw material production. Fig. 32.3 shows that future GDP growth will mainly come from the tertiary sector and nonenergy intensive industries, such as electronic products and light industry manufacturing. Based on this study, it is predicted that many energy intensive products will peak between 2020 and 2025, assuming that in future, the export of energy intensive products will not significantly increase considering that it is already a major contributor to global output. Table 32.2 presents the products output covering major energy intensive products; the analysis was based on physical unit input/output (I/O) tables, with a consideration for future infrastructure and consumption development. For example, in the scenario, building floor space was set up to be 89 × 109 m2 (82 billion m2) in 2050; when it is predicted that China will be well developed and personal income will be relatively high, with a per capita floor space of 64 m2. Following on, the newly build buildings per year will reach a peak before 2020 and then start to decrease. As 55% of all steel and 70% of all cement is used in building construction, and as many other energy intensive products are also closely linked to building construction, it stands to reason that when newly build buildings reaches a peak, then the demand for many energy intensive products will also peak. Furthermore, as the output of many consumption goods in China makes up more than half of the global output (see Table 32.3), there is little room future increase. The share of the value-added contribution of energy intensive industries of the GDP will decrease from 11% in 2010 to be less than 6% in 2050.

Table 32.2. Energy intensive products output scenario in IPAC.

Unit2005201020142020203020402050Crude steel106 t (million tonne)355627813710570440360Cement106 t106018682490295016001200900Glass106399580831740690670580Copper106 t2.64.797.957.676.54.6Aluminum106 t8.5116.9524.3825171512Lead and zinc106 t5.18.9-10.051076.55.5Sodium carbonate106 t14.6720.3425.252524.523.522Caustic soda106 t12.622.2830.6330252524Paper and paperboard106 t62.0592.7117.85110110105100Chemical fertilizer106 t52.263.3868.7664595653Ethylene106 t7.5614.1216.9624232323Ammonia106 t46.349.6556.9952505045Calcium carbide106 t8.514.725.22216117

Table 32.3. Share of outputs of selected goods from China in the world in 2015.

ProductsOutputShare of the worldCrude steel804 × 106 t49.6%Steel products1123 million ton70.5%Cement2359 million ton51.3%Aluminum31.41 million ton56.6%Copper7.96 million ton34.4%Zinc6.15 million ton43.8%Vehicles24.5 million27%Computer314 million90.6%TV145 million48.8%Refrigerater79.9 million71.8%Air conditioner142 million92.1%Washing machine72.7 million64.7%Microwave oven77.5 million75%

At present, energy intensive products are consuming nearly 50% of energy in China, so if there is no significant increase in the production of energy intensive products, with a much lower growth than GDP, the energy used in these energy intensive products will also limited. This will result in a large decrease in energy intensity per GDP and a resultant decrease in CO2 emission.

Table 32.4 presents the key parameters and data related to urban households in an economically developed China. The data are based on population growth, size of household, and personal income. From an analysis of data for households in developed countries is predicted that by 2030, due to an increase in income, urban households in China will have a similar quality of life to that in developed countries. This concerns issues such as electronic appliances, space heating, space cooling, etc.

Table 32.4. Urban household parameters in IPAC. Here HH refers to households.

ServiceUnitService202020302050HouseholdMillion288336380Share of HH with space heating42%44%48%Index of space heating intensityIndex for 2000 is taken as 11.351.51.6Index of space heating timeIndex for 2000 is taken as 11.331.361.4Share of building with 50% efficiency standard%204565Ownership of air ConditionerPer 100 HH130180260Index of air conditioner intensityIndex for 2000 is taken as 11.31.41.6Index of air conditioner utilization timeIndex for 2000 is taken as 11.61.82.2Ownership of Refrigeratorper 100 HH100120130Average space of refrigeratorL250310390Efficiency of refrigeratorkWh/day0.80.80.7Ownership of washing machineper 100 HH100100100Times to use washing machine per weekHours5.488Ownership of TVper 100 HH180220290Average capacity of TVW320300280Hours per TV per dayHours3.53.22.9Penetration rate of CFLa%100%100%100%Lightper HH142127Ownership of water heaterper 100 HH100100100Ownership of solar heaterper 100 HH182533Ownership of electric cookingper 100 HH130140260Hours per day of electric cookingMinutes123050Capacity of other electric applianceW150018002300Hours of other electric applianceMinutes5080100

3.2.1 Trends in Energy Intensity and Consumption per Capita

China has demonstrated significant improvements in energy efficiency since the 1980s. Energy use per unit GDP declined from 2.18 toe per thousand U.S. dollars in 1980 to only 0.73 in 2000, as shown in Fig. 8. During this period, commercial energy consumption increased 2.3 times, whereas the GDP increased more than six times. However, commercial energy intensity in China is double that of OECD countries and triple if noncommercial energy is included (Fig. 4). Many factors affect energy intensity, including economic structure, product mix, exchange rates, energy resource mix, and technical efficiency. In general, industrial sectors, especially heavy industry such as the iron and steel industry and cement industry, consume more energy per unit of economic output than do light manufacturing, finance, and service sectors. In China, for example, the relative energy consumption per unit of value added in primary (e.g., resource extraction and beneficiation), secondary (e.g., manufacturing and construction), and tertiary (e.g., finance and services) sectors was 12, 68, and 20, respectively, in 1999.

Changes in economic structure have contributed significantly to China's energy intensity improvement. Fig. 9 shows that economic structure has changed in China, with a decline in primary industry replaced by rapid growth in the tertiary sector. This trend is expected to continue, and the share of tertiary activities in the total economy will increase. Even within the secondary sector, subsectors with higher value-added and lower energy intensity, such as computer manufacturing and telecommunications equipment production, are developing faster than the traditional heavier manufacturing industries.

China is one of the few countries where coal is the dominant resource for energy use, which is part of reason for China's high energy intensity. For example, even using the latest technology, the highest energy efficiency for combined heat and power generation based on coal as a resource is 60%. Natural gas, on the other hand, can obtain conversion efficiencies of more than 80%. China is currently promoting clean energy production and importing cleaner technologies, which will result in energy efficiency improvements. In Southeast Asian countries, energy intensity is much lower than that of China. Because of the financial crises in the mid-1990s, however, the primary energy consumption per GDP increased, as shown in Fig. 10.

To provide a regulatory framework for energy efficiency interventions, many Southeast Asian countries and China have enacted a range of energy efficiency laws, with differences based on culture, political ideology, and existing institutional structures. Although the scope varies, common provisions include mandatory auditing for industries, mandatory energy building codes, developing performance standards for energy-using equipment, information dissemination, and capacity building. In most cases, implementing the legislation is carried out by existing energy agencies involved in energy efficiency promotion. In Thailand, an energy conservation fund to finance activities and programs for energy efficiency promotion has been set up, whereas for other countries the law specifies budget allocations for energy efficiency programs. For countries without specific legislation, energy efficiency activities are limited to programs that do not require specific regulation, such as publicity campaigns, demonstration projects, and programs that can be funded by external grants and loans, government budgets, or payments by consumers and end users. Some energy efficiency programs, such as mandatory appliance standards, appliance labeling, and building energy codes in the Philippines, Malaysia, and Singapore, are implemented through regulation but without reference to any specific law. A wide variety of policy instruments are used in promoting energy efficiency and accelerating the diffusion of energy-efficient technologies. These include appliances standards and building energy codes, appliance labeling, and financial incentives.

Since opening up to the outside world, the Chinese government has paid significant attention to energy conservation projects, including energy policy objectives to “carry out conservation and development of resources simultaneously but giving priority to conservation.” To address energy shortages, environmental protection, and sustainable development, the Energy Conservation Law was passed in 1997, providing broad guidance for energy efficiency improvement in all sectors. Since then, governments at national, provincial, and local levels and industrial associations have issued a number of energy efficiency policies and adopted measures for the law's implementation.

2 Methodology

2.1 Superstructure representation

Urban energy systems should have commonalities of general energy systems and particularities of urban systems. In the past, different energy systems were designed separately and operated individually. However, urban energy systems are multi-energy systems that encompass a variety of energy sources, needs, and conversion paths. While centralised generation still dominates the energy supply, distributed generation is playing a more critical role. Energy systems of the future should utilise the strengths of both types of production. Planning decisions, usually made on an annual or longer basis, should be able to meet the energy demand in each time slot under real operating constraints, such as capacity range and ramping rate limit.

Figure 1 illustrates the superstructure representation of the integrated urban energy systems. We use the adjective ‘integrated’ to highlight the interaction of multiple energy forms, the synthesis of centralised and distributed generation, and the coordination of design and operation phases. The two white blocks represent two different regions inside the city, each of which can be considered as an energy hub. Energy can be imported from outside the city, collected on-site, or transmitted and distributed between regions through energy networks. Inside each energy hub, there are energy generation, conversion, and storage sectors. Different varieties of primary energy (both renewable and non-renewable) are transformed into secondary energy (e.g. electricity, heat, and cooling) in the generation sector. In the conversion sector, secondary energy can be converted from one form into other forms. Storage facilities help to store the excess energy at a time for later use. These three sectors, as well as energy import and energy networks, work together to meet the final energy demand in each region.

Figure 1. Structure of integrated urban energy systems

10. RELATED LITERATURE

The central advantage of production economies for the understanding of the pattern of financial returns is the added discipline they present to the exercise. Since the actions of the same economic agents give rise to both macroeconomic and financial phenomena, it is a minimum expression of consistency that the same model be expected to replicate the financial and macroeconomic stylized facts, at least along a limited set of dimensions. This has been our perspective. In this section, we discuss other theoretical contributions with significant labor market features and their implications for financial return data. Related comments focusing on empirical contributions to the literature may be found in the essay by John Cochrane in this volume.

Matching financial data in a production setting requires that the capital owner display a strong desire to smooth his consumption intertemporally (provoked by, e.g., a habit formation feature) while simultaneously acting in a context that makes it difficult to reallocate labor or capital to that same end. These latter restrictions essentially substitute for some form of market incompleteness: in either case, agents are prevented from smoothing their consumption across states and dates. In most models, it is the degree of restrictiveness in the labor market that ultimately holds sway vis-a-vis financial characteristics. There are four models, in particular, that we review; principal comparative output data is provided as available. In all cases, notation is harmonized to be consistent with that adopted in this paper.

A first paper to emphasize the influence of labor market phenomena on equilibrium financial returns was Danthine et al. (1992). It proposed a model with shareholders, primary and secondary workers. These latter groups hold no securities (limited participation incompleteness). The primary workers are assumed to have a permanent, full employment association with the firm. Their compensation is governed by a risk sharing arrangement identical to the one proposed in this paper. At the other extreme, the secondary workers' employment prospects are governed by a pure Walrasian mechanism, one that otherwise would lead to substantial income variation. In order to moderate this wage income variability, primary worker wages are postulated to be subject to a wage floor augmented by unemployment compensation (the wage floor is above the market clearing wage in some states) financed by a tax on corporate profits.22 As a result of these latter arrangements, all workers in the model experience income volatility less than what would occur under a full Walrasian scenario. Whether directly—via wage insurance—or indirectly—via the unemployment tax—the net effect of worker income stabilization is to shift income risk onto the shareholders. The principle model results are presented in Table 12.

TABLE 12. Model Results: Danthine et al. (1992)i

Financial and aggregate statistics(a)ii(b)re4.560.84rf3.980.80rp0.580.06ddiii—5.36(d)corr(re, Ct+1/Ct)0.06(e)(f)Output1.760.69Total consumption0.340.98Shareholder consumption5.360.99Investment6.080.99Wagesiv0.220.10Capital stock0.540.03

While the model is able to replicate the stylized business cycle facts very well and produces a premium substantially in excess of what is obtainable under a Hansen (1985) construct, the premium obviously falls significantly short of what is observed. Security return volatilities are also much too low. In effect, variable equilibrium labor supply in the secondary sector in conjunction with shareholder control over investment together provide too much opportunity for shareholder consumption smoothing. Indeed, shareholder consumption volatility is about half the level of the benchmark case of this paper (Table 3, Case (1)); otherwise, the macro series are very similar. In a sense, the current model is a simplified version of Danthine et al. (1992) where all workers are subject to the primary worker income determination mechanism, augmented with an extra source of risk affecting the mechanism of income sharing itself. This second source of uncertainty is fundamental to its superior results along the financial dimensions.23

Boldrin and Horvath (1995) propose a contracting mechanism that is similar to Danthine et al. (1992). In equilibrium, it also has the consequence that employees supply resources to firm owners in high-income states and receive payments from them in low-output ones.24 In their setup, profits and hours both display high levels of variability in line with their respective empirical counterparts. As they do not present data on the pattern of financial returns characteristic of their model, it is difficult to directly compare their results with the other literature. By the nature of their model formulation, however, it is likely that their results would be similar to those in Danthine et al. (1992).

Subsequent to Danthine et al. (1992), the literature approached the same set of issues more from the perspective of modifying shareholder preferences in order that they act in a more risk-averse fashion and less from the “operating leverage” perspective of worker income insurance. The first paper in this tradition was Jermann (1998), which postulates a representative agent style model with habit formation (leading to high MRS volatility) in conjunction with capital adjustment costs that make it difficult to smooth consumption via investment variation. The inability of the agent to smooth is strengthened by a fixed labor supply assumption. With these features, his model is able to explain the business cycle stylized facts in conjunction with the mean premium quite well, but at a cost of excessive risk-free rate volatility. See Table 13.

TABLE 13. Model Results: Jermann (1998)i

Financial and aggregate statistics(a)ii(b)re7.0019.86rf0.8211.64rp6.18—dt+1/dt—8.44(e)Output1.76Consumption0.86Investment4.64

Boldrin et al. (2001) demonstrate, however, that the high premium in Jermann (1998) is significantly reduced if a Hansen (1985)-style labor-leisure choice mechanism is introduced even while retaining the same adjustment cost specification. Thus modified, Jermann's (1998) model also has the counterfactual feature that hours and output are negatively correlated. In this modified model, there are two opportunities for the representative agent (and therefore the representative shareholder) to smooth his consumption stream—by adjusting his hours and investment (though at a cost)—and, taken together, these are very effective consumption smoothing devices. As a result, the premium declines to 0.30 percent. The results in Jermann (1998) are thus not extremely robust. Jermann (1998) is nevertheless, important for establishing the basic modeling perspective for finance cum production models: make the security holders extremely desirous of smoothing their consumption in tandem with technological impediments to doing so.

Boldrin et al. (2001) also review a number of possible model features and ultimately explore one with two sectors—one producing consumption and the other capital goods—where the allocation of capital and labor to each sector must be chosen one period in advance of knowing the respective technology shocks. This has the consequence of reducing the ability of shifts in either factor of production to be used to smooth consumption significantly. It is the restrictions on labor market flows between sectors subsequent to shock realizations, in particular, that they view as most crucial to their results. In conjunction with standard habit formation preferences, these authors can explain the mean equity premium although investment volatility is a bit too low and the risk-free rate again displays excessive volatility, so much so that its standard deviation substantially exceeds that of the return on equity (see Table 14). We note that the excessive risk-free rate volatility of Jermann (1998) and Boldrin et al. (2001) is not a general consequence of the distributional risk perspective.

TABLE 14. Model Results: Boldrin et al. (2001)i

Financial and aggregate statistics(a)ii(b)re7.8318.4rf1.2024.6rp6.63—(e)(f)Output1.97—Total consumption1.360.76Investment4.710.96Hours1.580.78

Danthine and Donaldson (2002) revisit the original question posed in Danthine et al. (1992): to what extent can operating leverage cum income share variation simultaneously explain the business cycle and financial market stylized facts? It is an exploration that is accomplished in a slightly more abstract setting than in Danthine et al. (1992), whereby the latter's elaborate labor market setup (temporary and permanent classes of workers, etc.) is summarized by a “net” risk sharing mechanism nearly identical to the one considered here. The present paper may be viewed as decomposing the general results in Danthine and Donaldson (2002) into the distributional and aggregate shock-related components. With several additional features, such as costs of adjusting the capital stock, Danthine and Donaldson (2002) also achieve an excellent and broad-based fit to the data (Table 15). Generally speaking, Table 15 replicates the one presented in Table 3 except that returns seem to conform to the data slightly less satisfactorily. This is attributable to the slightly lower W/Y ratio, which results in a more modest operating leverage effect.

TABLE 15. Model Results: Danthine and Donaldson (2002)i

Financial and aggregate statistics(a)ii(b)(c)re5.9222.200.26rf2.464.050.02rp3.4622.34—dt+1/dt—16.72—(d)W/Y0.694.83−0.022(e)(f)Output1.77Total consumption1.450.96Shareholder consumption11.940.38Investment3.050.93Capital stock0.27−0.005

Our final theoretical comments concern Guvenen (2005). He assumes a perspective that may be viewed as providing an alternative macro interpretation for the variable risk sharing feature of the present model. Rather than assuming workers and shareholders interacting in an uncertain bargaining context, Guvenen (2005) presumes that the population is divided into two groups with unequal financial market access.25Shareholders participate in both stock and bond markets while non-shareholders trade only bonds. Both groups supply labor inelastically to the firm, and non-stockholders are modeled as being more risk-averse.26 With bond trading being their only mechanism for consumption smoothing, non-stockholders bid up bond prices, resulting in a low risk-free rate. In equilibrium, stockholders end up insuring non-stockholders by increasing their debt holding exactly when a low productivity realization reduces both agents' income, and vice versa. As a result, bond market events act to create a high level of volatility of shareholder consumption, volatility against which they can insure only via management of the capital stock. Although the effective extent of income insurance provided by shareholders to non-shareholders is not as great as in the present model, the fundamental idea is the same. Guvenen (2005) also goes on to show that the consumption of non-shareholders serves a role similar to that of a slow-moving habit in his equilibrium asset pricing equation, a feature also present in our distributional risk sharing formulation. We note that these results seem to be more favorable vis-a-vis “distribution risk” along the dimension of the return volatilities, but less so with regard to the business cycle stylized facts. In particular, investment is insufficiently volatile.

The substance of these theoretical contributions, broadly speaking, is as follows: (1) labor market arrangements have substantial impact on the volatility of profits and shareholder income. (2) In contexts where shareholders have limited ability to hedge this added income risk, its consequences for the equilibrium pricing of financial claims are profound and generally go in the direction of enhancing the models abilities to simultaneously replicate the stylized facts of the business cycle and financial markets. (3) Since the magnitude of the equity premium responds directly to low-frequency income shocks, it is convenient—in the sense of allowing for a superior replication of financial data within a simple context—to have alternative sources of income variation beyond that arising from business cycle co-movements. Our risk sharing mechanism is one such source. (4) A reasonable representation of the financial stylized facts requires income shocks that cannot be insured (smoothed). This may take the form of technological restrictions, as in many of the papers detailed in the present section, or various forms of market incompleteness. Our distribution risk perspective entails aspects of both these perspectives.27

TABLE 16. Model Results: Guvenen (2005)i

Financial and aggregate statistics(a)ii(b)(c)re5.3014.10—rf1.985.73—rp3.3214.700.30(e)Output2.4Total consumption2.3Shareholder consumption4.6Non-shareholder consumption1.1Investment2.7Capital stock—

The focus of research in empirical finance is to explain the cross section of security returns, where the notion of cross section is in reference to sets of specifically constructed portfolios rather than individual issues.28 Curiously, there are to date few studies that include labor market explanatory variables of any sort in the first stages of the Fama and MacBeth (1972) style regressions, which constitute the fundamental technique employed in these exercises. There are two exceptions to this general rule: Jagannathan and Wang (1996) and Santos and Veronesi (2004). Jagannathan and Wang (1996) include the growth rate in per capita labor income as an explanatory variable, a fact that allows their model to outperform the standard CAPM. Such a variable is completely consistent with the model presented in this paper: an above-average value of μ¯tin a particular period is consistent with a simultaneous high growth rate in per worker labor income, and vice versa.

Santos and Veronesi (2004) focus rather on the predictability of stock returns. Their labor market variable is the economy-wide labor income to total consumption ratio, a quantity that is perfectly positively correlated with μtin our model. What is of particular interest to us is the intuition provided by Santos and Veronesi (2004), all of which, by construction, applies to the “distributional risk” construct. They argue that the share of income due to wages, as with all other principal sectors of the economy is a stationary process. The significance of this fact for asset pricing is twofold: (1) if the share of income to labor is high and likely to remain so, investors' MRS variability will be relatively insensitive to events in the stock market, and thus the market risk premium is likely to be small, and vice versa. We note, however, that this ignores the operating leverage effect: a higher share of income to labor suggests a higher fundamental riskiness in the equity cash flow, something, per se, likely to increase the premium. For most all cases presented in this chapter, the latter effect dominates the Santos and Veronesi (2004) intuition. (2) If the share of income from wages is above average, it is likely to decline with the consequence that future dividend growth is likely to exceed consumption growth, leading to high asset prices and returns. The time variation in the asset risk premium suggested by these comments, however, is fully a feature of the distributional risk model.

Regressing stock returns on lagged values of this variable leads to statistically significant coefficients and adjusted R2 that exceed what would be obtained using, e.g., the lagged dividend-price ratio as the explanatory variable. We suspect that a similarly good fit could be obtained using data generated by our model. Including this ratio as an explanatory variable also allows the model to outperform the standard CAPM in explaining the returns to the 25 Fama and French (1993) portfolios. Given the results obtained here, none of this is surprising.

6.5 Nuclear-Based Cogeneration Systems

Cogeneration, which is the simultaneous production of thermal and electrical energy, has significant potential for transforming present energy supply systems so that they become more sustainable. In any type of thermal power plant a portion, usually 20–45%, of the input thermal energy is converted to electricity, and the remainder is rejected to the environment as waste heat. As nuclear power generation units can produce a high-pressure and high-temperature vapor, they can be utilized to generate heating, cooling, or freshwater with a topping cycle. In this chapter, we try to address the main nuclear-based cogeneration plants briefly. Besides power these cogeneration plants co-produce heating, freshwater, cooling, hydrogen, process heat, synthetic fuels or other commodities with market value. In Chapter 9 another perspective on the cogeneration system will be provided, when discussing multigeneration systems.

When cogeneration is applied better resource utilization is obtained. This is illustrated in Figure 6.25. In a cogeneration system the thermodynamic cycle can be connected to two heat sinks. One heat sink is the reference environment, typically air or a lake or river. The heat engine rejects heat at a temperature higher than the environment depending on the specific demand. In district heating or desalination applications the required temperature level is low (60–120 °C). In other applications heat can be required at intermediate or very high values. Obviously, the applications with a low-grade heat demand are the most favorable economically because valorizing the low grade heat will increase the return on investment in the nuclear power plant and make it more economically attractive.

A short numerical example can be given related to options (a) and (b) presented in Figure 6.26. Assume that there is a demand of 300 MW power and 350 MW heat. If heat and power are produced with separate technologies (for example, power is derived from a nuclear station and heat is generated by a natural-gas-burning furnace) then the following situation is valid: for 300 MW power the required thermal energy is 900 MW, and for 350 MW heating one needs to combust 410 MW equivalent natural gas for 85% furnace efficiency. Hence the overall efficiency is 650/1310 MW = 49.6%. With a cogeneration approach there is no need for natural gas, but rather 350 MW of heat is recovered from the nuclear power plant; note that in order to produce 300 MW power the power plant must reject into the environment about 600 MW of waste heat. Hence cogeneration is motivated by an efficiency of 650/900 MW = 72%.

Although there are some challenges, such as coping with the danger of radioactive contamination, there is obviously great potential for nuclear heat. Cogeneration with nuclear energy has been used in the past and is used today in a limited number of applications. One of the most common uses of nuclear-based cogeneration is for district heating. The worldwide experience with nuclear district heating measures 500 reactor-years, which represents only 4.1% of the global total of 12,193 reactor-years, according to IAEA (2006). Nuclear desalination has also been used in some countries with a cumulated experience of 250 reactor-years (~ 2% of global nuclear power station experience). Some limited experience exists for industrial heat cogeneration with nuclear reactors, namely for heavy water production, industrial steam cogeneration, salt refining, and cardboard production. Two main categories of heat demand can be satisfied through cogeneration:

Tertiary sector, more specifically including heat demands in residential, commercial, governmental building settings (water and space heating or air conditioning).

Primary and secondary sectors that require heat at a wide temperature range and capacities for water heating, space heating, drying processes, manufacturing, mining, agriculture, chemical processing, etc.

Figure 6.27 shows the temperature level with Generation IV nuclear reactors versus requirements of some relevant heat demanding processes. When heat is transported remotely from the nuclear reactor to the user site measures must be taken to avoid any possibility of radioactive contamination through the heat transfer fluid. Two configurations are suggested for reactor radioactive isolation, as shown in Figure 6.28. In the HLH configuration there is a higher pressure in the reactor loop and process loop. Possibly, radioactive contaminants may leak into the isolation loop; however, due to the pressure, contamination by radioactive particles remains confined in the isolation loop and does not pass further. The isolation loops may be treated for cleaning with specific methods. With the LHL configuration the isolation loop has the highest pressure and therefore will not allow penetration of particles or fluids from the other two loops.

Figure 6.29 suggests a possible linkage between a nuclear Rankine steam power plant and a heat- demanding system for cogeneration. The steam generator which produces superheated steam is part of the multistage Rankine power plant. A reheating circuit can be integrated with the steam generator. When cogeneration is required, a part of the superheated steam from the steam reheating circuit is directed toward a secondary low-pressure turbine (stream #27). Steam is expanded to an intermediate pressure in accordance with the temperature level required for cogeneration. After a heat transfer process with the cogeneration loop steam fully condenses and the liquid water is returned at the steam trap of the open heat water feeder. For applications where a higher temperature is required for the cogeneration loop the low-pressure turbine is not used, rather the cogeneration heat exchanger is supplied directly with high-temperature steam (Figure 6.29).

The power and heat products of a nuclear reactor station can be converted in industrial parks into multiple commodities such as steam, potable water, and hydrogen and used further for many processes, as suggested in Figure 6.30. Some examples of heat-demanding industrial processes are as follows:

Aluminum production: the alumina-rich bauxite ore (Al2O3) must be extracted in slurry form mixed with sodium hydroxide at 110°–270 °C, with the intermediary products being sodium aluminate and aluminum hydroxide. Further, a rotating kiln is used for calcination of aluminum hydroxide at 1100 °C.

Steam-coal gasification: this process can be conducted in two ways (i) steam-coal gasification and (ii) hydrogasification. In hydrogasification coal is combined with hydrogen in an exothermic reaction to produce synthetic natural gas. Nuclear heat is used to produce synthesis gas and extract hydrogen through steam reforming and water-gas shift reactions. The processes can be coupled with the VHTR for a required heat of 950 °C.

Steam-assisted gravity drainage: this process is used to extract bituminous oil sands where steam at 10–15 MPa and 200–240 °C is required. In addition this process requires hydrogen for synthetic crude oil production.

As the population is growing, many regions are experiencing increased freshwater demands that greatly exceed the supply capability of existing infrastructures. The problem is compounded by increases in both the pollution and salinity of freshwater resources. Lack of freshwater is a prime factor inhibiting regional economic development. Seawater desalination is an important option for satisfying current and future demands for freshwater in arid regions in close proximity to the sea.

Desalination units need energy to separate salt from saline waters. The energy can be either heat for seawater distillation or mechanical energy to drive pumps for pressurization of seawater across membranes using reverse osmosis (RO). The major source of heat energy comes from fossil fuels, including coal, oil, and gas. Most of the large-size plants based on thermal and membrane processes are located near thermal power stations, which utilize fossil fuels to supply both steam and electrical power for desalination. However, the use of fossil fuel to produce freshwater has several side effects. As the combustion of fossil fuels emits greenhouse gases to the environment, it will lead to global warming, one of the main concerns of this century. In addition, these fuels are non-renewable and will be exhausted in next 50 years.

Therefore, because of their versatility as an energy source and hydrocarbon feedstock, there is a keen interest in conserving fossil fuels for other industrial applications, especially in countries with inadequate sources of energy. Over the long term it is not practical to establish a desalting economy based solely on fossil fuels because they have limited availability, whereas freshwater must continue to be available to sustain humankind. Long-term reliance on desalting should only occur if the availability of the energy supply is comparable to the required availability of the product. This criterion can be satisfied by nuclear energy. Interest in using nuclear energy for producing potable water has been growing worldwide in the past decade. This has been motivated by a wide variety of factors, from the economic competitiveness of nuclear energy to energy supply diversification; from conservation of limited fossil fuel resources to environmental protection; and by the spin-off effects of nuclear technology in industrial development. One of the main cogeneration units using nuclear power as a topping cycle is used to produce freshwater by multistageflash desalination techniques.

Hydrogen is expected to play a significant role as energy carrier in the future. Hydrogen can be used as fuel in almost every application where fossil fuels are utilized today. In contrast to fossil fuel, its combustion is without harmful emissions, disregarding NOx emissions that can be effectively controlled. In addition, hydrogen can be transformed into useful forms of energy more efficiently than fossil fuels. Hydrogen is as safe as other common fuels, despite its common perception. Hydrogen is not an energy source and hence it does not exist in nature in its elemental form.

Therefore, hydrogen must be produced from water, the most abundant source of hydrogen, or from other sources. However, splitting of water for hydrogen production necessitates energy that is higher than the energy that can be obtained from the produced hydrogen. Therefore, hydrogen is considered an energy carrier for a suitable form of energy like electricity. It is commonly accepted that hydrogen is one of the promising energy carriers and that the demand for it will increase greatly in the near future, for it can be utilized as a clean fuel in diverse energy end-use sectors, including the conversion to electricity with no CO2 emission. In addition, the nuclear fuel supply is estimated to be readily available even on a once-through fuel cycle for 50 to 100 years. Using breeder reactors and a closed fuel cycle, it is virtually inexhaustible. Because of its relative abundance, nuclear fuel is relatively cheap compared to fossil-based fuels.

Possible routes to generate hydrogen from water using nuclear energy are also depicted in Figure 6.31, with additional paths which assume processes other than water decomposition, such as coal gasification, natural gas reforming, petroleum naphtha reforming, or hydrogen sulfide cracking. In all such processes, high-temperature nuclear heat is used to conduct the required chemical reactions. Coal gasification and natural gas reforming methods include the water-gas shift reaction to generate additional hydrogen and reduce the carbon monoxide to CO2. Nuclear-based reforming of fossil fuels is a promising method of high viability in the transition toward a fully implemented hydrogen economy which will reduce the world dependence on fossil fuel energy.

Although fossil fuel reforming can be used to generate hydrogen, it is attractive to use nuclear heat to synthesize Fischer–Tropsch fuels or methanol. Such fuels can also be used for transportation vehicles. With this method, nuclear heat is used to generate synthesis gas, and then to generate either diesel or methanol. Another process of great interest is fertilizer production, such as ammonia and urea, with nuclear energy. A nuclear ammonia/urea production facility would comprise the nuclear reactor, a heat transfer system for high-temperature nuclear process heat, a nuclear power plant, a nuclear hydrogen generation unit, an air separation unit, H2/CO2 separators, a Haber–Bosch synthesis unit, and an NH3/CO2 reactor to generate urea.

Hydrogen sulfide is a potential source of hydrogen. Hydrogen sulfide can be extracted from some geothermal wells, oil wells, volcano sites, and seas. The Black Sea represents the highest reserve of hydrogen sulfide in the world. In the Black Sea, the hydrogen sulfide can be viewed as a renewable resource because it is generated by microorganisms under the influence of solar radiation. Typically, thermo-catalytic cracking of H2S can be applied to generate hydrogen and sulfur, as two valuable products using nuclear energy.

Case Study 6.3: Integrated Desalination and Hydrogen Production with Nuclear Reactor

In this example, which is based on the work by Orhan et al. (2010), we analyze a coupling of the Cu–Cl cycle with a desalination plant for hydrogen production from nuclear energy and seawater. To produce hydrogen by splitting the water molecule one needs to supply freshwater to the hydrogen production facility. Thus, to avoid causing one problem while solving another problem, hydrogen could be produced from seawater rather than limited freshwater sources. Nuclear energy is used to drive the process. Here we consider five configurations, in hopes of determining or helping to determine an optimum option to couple the Cu–Cl cycle with a desalination plant.

Configuration 1: the Cu–Cl cycle is coupled to a desalination plant using nuclear energy, as shown in Figure 6.32. Salty water is put into the desalination plant and salt is removed from the water using waste energy from the nuclear reactor moderator at 70–80 °C. Freshwater supplied by the desalination plant is decomposed into hydrogen and oxygen by the Cu–Cl cycle driven by nuclear energy. humidification–dehumidification (HD) technology is used for desalination.

Configuration 2: the thermal energy recovered from the Cu–Cl cycle is transferred to the desalination plant to remove salt from freshwater. As illustrated in Figure 6.33, the desalination plant operates as a subsystem of the Cu–Cl cycle. Considering the overall system, only process/waste energy from the nuclear reactor and salty water enter the system. Hydrogen is produced, and oxygen and salt are byproducts. A drawback to this configuration is the efficiency decrease (~ 3–5%) incurred by the Cu–Cl cycle as the recovered energy is used for the desalination process rather than within the cycle itself. But the Cu–Cu cycle is a subsection of the plant and, when the desalination plant and the Cu–Cl cycle are considered as a combined system, the efficiency is not affected significantly as the recovered energy is used within the overall system. The efficiency of the combined system (desalination plant and copper chlorine cycle) is about 0.4. Multiple effect desalination technology is used.

Configuration 3: the nuclear thermal energy is used directly in the desalination plant and to drive the hydrogen plant as indicated in Figure 6.34. The thermal energy recovered in the Cu–Cl cycle is used within that cycle. A desalination method with a high capacity and low production cost is used as high-grade energy is available. Multistage flash desalination method is used.

Configuration 4: in addition to nuclear thermal energy, solar energy is used to complete the energy requirement for the process. The solar energy drives directly and completely the desalination plant which supplies the hydrogen production plant with freshwater. Process and waste energy from a nuclear plant are used in the Cu–Cl cycle as indicated in Figure 6.35. Vapor compression (VC) desalination technology is applied.

A drawback of this configuration is its dependence on the availability of solar energy, which is intermittent. As the capacity of the desalination plant and hence the Cu–Cl cycle determines the required capacity of the solar collectors, the site for the Cu–Cl cycle and desalination plant is chosen carefully. However, as constraints likely exist regarding the location of the nuclear reactor, this configuration is likely advantageous if the nuclear reactor is located in an area with high solar insolation. Otherwise, a desalination process with low capacity and low inlet energy requirements is used. RO technology is applied.

Configuration 5: off-peak electricity from a nuclear reactor is used to operate desalination plant. When off-peak electricity is available, it is usually less expensive than peak electricity and thus is used by many industrial processes. Off-peak electricity can also be used to desalinate seawater and to produce hydrogen, and could be beneficial for the same reason. This configuration includes a Cu–Cl cycle coupled with a desalination plant driven by off-peak electricity (as in Figure 6.36). In this case, efficient membrane processes are used as electrical energy is supplied.

The overall energy efficiency of the coupled system, ηoverall, represents the fraction of energy supplied to produce hydrogen from salted water that is recovered in the energy content of H2 based on its lower heating value. The total energy required to produce hydrogen from salty water can be written as

Which sector of the economy takes raw materials into manufactured goods?

Which sector of the economy is involved in manufacturing?

What is the sector that gains the raw materials and transformed them into manufactured goods?