Classification of saline waters.
\r\n\tThe purpose of this book is to discuss some of the critical security challenges in today’s computing world and to discuss mechanisms for defending against those attacks by using classical and modern approaches to cryptography and other security solutions. With this objective, the book invites contributions from researchers in the field of cryptography and its applications in network security. Some illustrative topics of interest (but not limited to) are cryptography algorithms, authentication, authorization, integrity, confidentiality, privacy, security in wireless networks, security in wireless local area networks, wireless sensor networks, wireless ad hoc networks, vehicular ad hoc networks, security and privacy in the Internet of Things. Privacy of information, Blockchains, and Machine Learning in Security are three additional topics that the book will also deal with.
",isbn:null,printIsbn:null,pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"b268e581d5e458cb91b82c518f2717eb",bookSignature:"Prof. Jaydip Sen",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11547.jpg",keywords:"Symmetric Key Cryptography, Block Ciphers, Authentication Protocols, Electronic Mail Security, User Privacy, Privacy-Preserving Data Mining, Blockchain Security, Anomaly Detection, Malware Analysis, Secure Quantum Communications, Internet of Things, Cyber Laws",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 4th 2022",dateEndSecondStepPublish:"June 1st 2022",dateEndThirdStepPublish:"July 31st 2022",dateEndFourthStepPublish:"October 19th 2022",dateEndFifthStepPublish:"December 18th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"6 days",secondStepPassed:!1,areRegistrationsClosed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Prof. Sen is a pioneering researcher in machine learning and artificial intelligence. 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Nearly 97% of the total water on earth is in oceans in the form of saline water and only the remaining 3% is fresh water. Out of this, nearly 70% of fresh water is in the form of ice present in the polar region and higher mountain ranges. Underground water constitutes 27% of fresh water and only <1% is in the form of surface water present in lakes and rivers. Rapid changes in human lifestyle coupled with urbanization and industrialization has created pressure on the limited fresh water resources. Further, the impending climate change has favoured salinization of both land and water across many parts of the world [1].
\nThe projection given by FAO indicated increase of food requirements as a result of burgeoning population by 20% in developed countries and 60% in developing countries. In other words food requirements are increasing quicker than crop production. Hence, there is urgent need to improve alternative agricultural strategies [2, 3]. Among the many reasons affecting agricultural productivity in the tropical region, salinity and associated factors, like waterlogging and/or drought exaggerated by climate change have contributed significantly. The increase in saline areas has been directly attributed to both water and soil salinity problems. In the coastal areas inundation of low-lying areas by sea water and sea water intrusion into the fresh water aquifers contribute to the coastal salinization.
\nSince the balance between water demand and water availability has reached critical levels in many regions of the world and increased demand for water and food production is likely in the future, a sustainable approach to water resource use and salinity management has become imperative [4]. A number of approaches have been developed to combat the salinity problems and increase the food grain production, based on specific types of site, regional and global problems. This chapter highlights concepts of water resources, its availability, human demand and use of fresh water, the effect of climate change and other factors on salinity and water resources in the future and also discusses the ways to manage this precious natural resource.
\nThe concept of water resources encompasses qualitative socio-economic and environmental dimensions besides its quantitative and physical aspects. The source of all forms of water either directly or indirectly is precipitation, often used interchangeable with total rainfall in literatures. However, with reference to water resources, precipitation is considered as gain, evapotranspiration is viewed as loss and human use including for agriculture is described as demand. When the resources are contaminated by human activities or turned into saline by natural means, the fresh water resources get reduced which intensify the water demand. At the same time part of the rainfall after reaching the ground get evapotranspired or moves to the fresh water resources (surface and ground water).
\nThere are several reports on global total fresh water resources which are estimated with reference to a particular year, and may vary with the progress of time as it is depend on several dynamic components. The total freshwater resources spread across the world are estimated to be in the order of 43,750 km3 year−1. At the continental level on an average America has the largest share of the world’s total freshwater resources with 45%, followed by Asia (28%), Europe (16%), and Africa (9%) [5]. Due to uneven distribution of population and water resources, continent wise estimation of water resource per inhabitant showed that America has highest amount with 24,000 m3 year−1 followed by Europe (9300 m3 year−1), Africa (5000 m3 year−1) and Asia (3400 m3 year−1) [6]. At the regional level, tropical humid region has fairly good IPWR per capita due to good amount of annual rainfall and water resources. However at the country level, countries located in the Arabian Gulf and Northern Africa (Morocco, Algeria, Bahrain, Jordan, Kuwait, Libyan Arab Jamahiriya, Maldives, Malta, Qatar, Saudi Arabia, United Arab Emirates and Yemen) are having very low total renewable water resources (TRWR) of 500 m3 per inhabitant. In terms of internal renewable resources (IRWR), the threshold of 1000 m3 per inhabitant is considered as water stress, which showed that countries located in North Africa and the Middle East are at the most critical stress level with values ranging from 0 to 1000 m3 year−1 per person.
\nWater is withdrawn from the available resources for various purposes which creates the demand. In other words, to understand the relation between supply and demand, the ratio between water withdrawal by agriculture, municipalities and industries over total renewable water resources is used. This also indicates the level of human pressure on water resources. Arid and semi-arid regions in Asia and Africa have maximum withdrawal of more than 90% of renewable water as given in Figure 1. In these areas surface flow is seasonal due to less rainfall. As a consequence this region exploits more ground water resources than other region.
\nPercentage of renewable water resources withdrawn.
Agriculture accounts for roughly 70% of total freshwater withdrawals globally and for over 90% in the majority of Least Developed Countries (LDCs) [7]. If remedial measures to improve the efficiency are not seriously implemented, by 2050 global agricultural water consumption is projected to increase by about 20%. Globally, some 38% of irrigated areas depend on groundwater [8] which has contributed to a 10-fold increase of groundwater abstraction for agricultural irrigation over the last 50 years. Conversely, almost half of the world’s population depends on groundwater for drinking consequently salinization or overexploitation will affect the freshwater availability for domestic purpose.
\nIn the context of global water resources, demand and salinity, it is imperative to define salinity as it various in intensity and the severity is different based on the intended purpose.
The term salinity used herein refers to the total dissolved concentration of inorganic ions (Na, Ca, Mg, K, HCO3, SO4 and Cl) in irrigation, drainage and ground waters. Individual concentrations of these cations and anions in a unit volume of water can be expressed either on a chemical equivalent basis (mmolc/l) or on a mass basis (mg/l). The total salt concentration in water is expressed either in terms of the sum of either the cations or anions (mmolc/l) or the sum of cations plus anions (mg/l). But for analytical convenience, salinity is measured as electrical conductivity (EC) expressed in units of (dS/m) [6]. As the solubility of salt vary at different temperature, EC values are always expressed at a standard temperature of 25°C to enable comparisons. This also helps to back convert EC into total salt concentration which is 1 dS/m = 10 mmolc/l = 700 mg/l. In spite of certain shortcomings, EC is a fair indicator of salinity as plants are mainly sensitive to total salt concentration rather than to the proportions of individual salt constituents. At the same time, for comparison purposes, ‘soil salinity’, is commonly expressed in terms of the electrical conductivity of an extract of a saturated paste (ECe; in dS/m) made using a sample of the soil. Normally due to the involvement of different modifying factors rigid water quality classifications are not advised but for the purpose of identifying the levels of water salinities water classification scheme is used.
\nIn terms of total salt concentration, which is the major water quality factor generally limiting the use of waters for crop production and other purposes, water classes are defined (Table 1). As per this scheme, only very hardy and tolerant crops can be successfully grown with waters having salinity of 10 dS/m in EC or more. Many drainage waters, including shallow ground waters underlying irrigated lands, fall in the range of 2–10 dS/m in EC. Such waters have good potential for selected crop production with suitable salinity management practices. Reuse of second-generation drainage waters for irrigation in selected locations is sometimes possible particularly for purposes of reducing drainage volume in preparation for ultimate disposal or treatment. Such waters will generally have ECs in the range 10–25 dS/m. Very highly saline waters (25–45 dS/m in EC) and brine (>45 dS/m in EC) are beyond the scope of these guidelines and their uses for crop production are therefore not discussed herein. In summary water having EC more than 10 are not recommended for irrigation and water with EC value <10 are used with suitable salinity management methods.
\nWater class | \nElectrical conductivity (dS/m) | \nSalt concentration (mg/l) | \nType of water | \n
---|---|---|---|
Non-saline | \n<0.7 | \n<500 | \nDrinking and irrigation water | \n
Slightly saline | \n0.7–2 | \n500–1500 | \nIrrigation water | \n
Moderately saline | \n2–10 | \n1500–7000 | \nPrimary drainage water and groundwater | \n
Highly saline | \n10–25 | \n7000–15,000 | \nSecondary drainage water and groundwater | \n
Very highly saline | \n25–45 | \n1 5000–35,000 | \nVery saline groundwater | \n
Brine | \n>45 | \n>45,000 | \nSeawater | \n
Classification of saline waters.
The availability of fresh water for farming is an essential condition for achieving satisfactory and profitable yields, both in terms of unit yields and quality. In coastal regions due to excessive withdrawal of ground water, high evapotranspiration, rise of saline ground water and sea water intrusion pose major challenge. The most common reasons for the increase in salt-affected lands are the mismanagement of irrigated areas. Increase in groundwater pumping results in the intrusion of seawater into the fresh water aquifers. In certain region/islands due to the exhaustion of fresh water aquifers the overlying saline water layers mix with fresh water, resulting in the increase of salinity in the groundwater. In the dry region, high rates of irrigation water application and inadequate or absence of drainage systems has resulted in the movement and deposition of salt on the surface of the soil profile favoured by high evapotranspiration rates. As a result nearly 5–10% of the existing fresh water resources are getting salinized. The critical values of renewable fresh water resources and economic water scarcity and salinization indicate the necessity for regional water use policy and appropriate water management strategies at various levels.
\nAs discussed above the salinity level, both soil and water, has been increasing in many of the regions particularly in the tropics and arid regions though the processes of occurrence of salt affected soils are different. Salinity can be found in different altitudes, from territories below sea level, e.g. the district of the Dead Sea, to mountains rising over 5000 m as the Tibetan Plateau of the Rocky Mountains [9]. Older estimates [10] suggest that 10% of the total arable land is affected by salinity and sodicity, extending over more than 100 countries occupying different proportions of their territory. The description of the types of salt-affected soils, causes of formation and hypothetical salinization cycle has been reported by many researchers [11].
\nDue to the non-availability of updated information or lack of compilation of regional level assessments the current extent of salt-affected soils are unknown. Based on the FAO/UNESCO Soil Map of the World, Massoud [12] made an estimate of 880 M ha of salt-affected soils of which 36% are in developing countries. These are the potential areas where land can be leased for food production or alternate energy sources using suitable technologies which are currently available. However, Balba [13] gave a global estimate of only 600 M ha as salt-affected soils which included 340 M ha in Asia, 140 M ha in Australia, 60 M ha in South America 30 M ha in Africa, 26 M ha in North America and 1 M ha in Europe. The recent estimate quoted 954.8 M ha of salt-affected soils which is much higher than the previous estimates [14].
\nSimilar to that of soils, the availability of freshwater is a major limiting factor for sustainable agriculture and other developmental activities. In certain regions of the world the water crisis is so severe than the availability of land. Unlike soils, there are several assessments and projection for future water requirements and availability. Global assessment of water availability and projections found decrease in water availability in the developing regions with increasing population pressure [15, 16]. The assessment grouped the water scarcity into physical water scarcity, approaching physical water scarcity and economic water scarcity to understand the water shortage which includes all purpose of water (Figure 2). Physical water scarcity means water resource development is approaching or has exceeded sustainable limits. Here water availability is related to water demand which implies that dry areas are not necessarily water scarce. This physical scarcity analysis showed that more than 75% of river flows are withdrawn for agriculture, industry and domestic purposes. In approaching physical water scarcity nearly 60% of river flows are withdrawn and these basins may experience physical water scarcity in the future. The situation may get worsen with more withdrawal to produce more food. Whereas in economic water scarcity even though water in nature is available locally to meet human demands factors such as human, institutional and financial capital limits access to water. The tropical developing region mostly faces the challenges of water scarcity. Economic water scarcity is the major limits for production in sub-Saharan Africa while physical water scarcity limits the production in South Asia.
\nAreas of physical and economical water scarcity at the basin level in 2007 [
In contrast to the crisis, some countries have developed technologies to utilize the saline water. For example, in Israel, farmers carryout crop production with unconventional water resources irrigation and desalination plants have been installed to get fresh water from saline water. In some tropical regions of Asia technologies have been developed to address this issue and we can find agriculture practices based on alternative plant species, most of them are halophytes, which are able to tolerate high temperatures and/or low water availability [17]. Similar attempts are being made in some of the South Asian countries to meet the challenges but in many cases these are at experimental stage.
\nLarge scale withdrawal of ground water (over exploitation), especially from the artesian aquifers can sometimes result in local land subsidence due to compression of the aquifers. Land subsidence poses serious problems to buildings, other structures and affects the equilibrium of freshwater-sea water interface region. Sometimes this causes inundation of low lying areas, resulting in sea water ingress. The subsidence depends on the nature of sub surface formations, their extent, magnitude and duration of the artesian pressure decline.
\nThis is one of the most serious emerging problems in the coastal regions. When groundwater is pumped out of coastal aquifers which is in hydraulic connection with the sea due to gradients salt water from the sea may flow towards the well (Figure 3). There is a dynamic equilibrium in the seawater-fresh water interface which gets disturbed due to over exploitation of ground water or reduced freshwater recharge. This result in movement of salt water into freshwater aquifers under the influence of groundwater development or by over exploitation which is known as seawater intrusion. Sometime there is a propensity to point out the occurrence of any saline water along the coastal zone to sea water intrusion. But there may be many reasons for the occurrence of salinity. In order to avoid mistaken diagnoses of seawater intrusion as evidenced by temporary increases of total dissolved salts, chloride-bicarbonate ratio as a criterion to evaluate the intrusion.
\nFresh water-salt water interface.
This phenomenon occurs due to the local rise of the interface between fresh and saline water. This happens when an aquifer having underlying layer of saline water is pumped by a well penetrating only the upper freshwater portion of the aquifer. This rise in interface layer below the well due to excess removal of water is called upcoming of saline water. Generally the interface lies near horizontal at the start of pumping which rises to progressively higher levels with continued pumping of water until eventually it reaches the well. At that point it necessitates closing of the well because of the degrading influence of the saline water. When pumping is stopped, the denser saline water tends to settle downward and to return to its former position. In such areas, the rainwater tend to float over saline water as a thin lens and in such conditions the saline water rises by 40 units for every unit of the fresh water withdrawn. Because of this very fragile ground water system of small islands the fresh water needs to be skimmed to prevent upcoming.
\nThis kind of salinity is a common water quality problem observed in the coastal aquifers. In these aquifers, the salinity is caused because of leaching of salts in the aquifer material. In certain areas the formation water gets freshened regularly due to the leaching effect. This happens mostly for the water soluble salts only.
\nThe observed and projected increase in mean sea level due to global warming poses a serious threat to the coastal aquifers particularly located in the small islands. The projected SLR will drive the fresh water-seawater interface more towards inland along coastal aquifers and consequently submerge the lowlying areas with saline sea water. This will result in direct salinization of shallow coastal aquifers. Water resources of the small islands located in the tropical region (Indian Ocean and Polynesian islands) will be significantly affected by the rise in sea level and with the change in rainfall pattern the negative effect will be even greater.
\nWater has become a scarce natural commodity due to its declining availability and increase in demand for various purposes. This has created huge pressure on the available fresh water resources around the globe. Several reports state that the magnitude of stress on water resources is expected to increase as a consequence of climate change, population growth, economic development and land-use change including urbanization [18]. In consequence several studies were carried out focusing on the assessment of global water demand and its availability. In reality water demand has reached critical levels in several parts of the world, particularly in countries with very limited water availability. Many researchers have concluded that besides climate change, misuse of water, over exploitation and limited infrastructures for water supply are the major reasons for water scarcity.
\nGlobally water consumption for all sectors amounts to 9% of total freshwater resources in the world with agriculture being the largest user, in turn accounting for approximately 70% of total water withdrawals which is equivalent to 2700 km3 year−1 [19]. Agricultural sector receives up to two-thirds of the total water withdrawals and accounts for almost 90% of the total water consumption in the world [5]. As more than 80% of global agricultural land is rainfed water demand is met mostly from the green water resource [16]. In Asia, Africa, Central and South America, the values for specific water withdrawal range from 50 to 100% which experiences great diversity in climatic conditions. Irrigation water withdrawals range between 96 km3 in Sub-Saharan Africa and 708 km3 in East Asia; the highest values for specific water withdrawal are observed in South Asia, with 913 km3 [20].
\nAnalysis of factors affecting water supply and demand indicated that the water demand will be influenced by population growth, industrial development and food production besides climate change. At the same time the water supply will be decided by land use change, ecological and economic restriction, pollution besides climate change (Figure 4). The balance between these two will decide the fresh water availability [21]. The future global water situation and development until 2025 was analysed with different scenarios. Under the business as usual scenario the present contrast in water situation between industrialized and developing countries is likely to continue in the future. Withdrawal of water from the available resources in most of the industrialized countries is projected to decline or will remain at the present level due to technological and efficient water management. Consequently the pressure on available freshwater resources will decline. In contrast withdrawal will continue to grow in developing countries due to urbanization and industrialization. Further, the push for development will also be expected to increase the salinity level. This will increase the pressure on the available freshwater resources by increasing severe water stress area from 36.4 to 38.6 million km2. The increase will be significant in Southern Africa, Western Africa and South Asia which will be a limiting factor in the future for industrial and agricultural growth due to competition for water [15]. On an average globally 40% water deficit will be experienced by 2030 under a business-as-usual scenario.
\nDriving forces of future water supply and demand (modified from Hornbogen and Schultz [
In spite of efforts by various stakeholders and global level organizations, lots of gap still persists in our understanding of the global water resources and the emerging salinity problems. Meanwhile there are several disputes in utilizing and sharing this precious resource. Human activities have rendered water unusable at several places due to pollution, salinity and over exploitation. There should be proper regulations and monitoring which involve measures like precaution/prevention; control/restriction and remedial/restoration measures. Efforts should be made to study sea-level rise and sea water intrusion. In summary, the available information suggests that water security and the salinity will remain a challenge for many tropical countries today and in the future until suitable remedial measures are implemented and relevant technologies are developed.
\nThe OECD1 countries have encountered many challenges throughout the years, including rapidly aging societies and diminishing fertility rates. The share of the senior population (those over 65 years old) climbed from less than 9% in 1960 to 17% in 2015, and it is anticipated to continue to rise, reaching 28% in 2050.2
Furthermore, since 1970, most OECD countries have faced the difficulty of a primarily urban population. In OECD countries, particularly Australia, Korea, Chile, France, and Japan, urbanization is higher, and the trend is anticipated to continue.3
Because demographic shifts pose serious distributional issues and are projected to have significant economic effects, OECD nations must take these factors into account.
Indeed, natural gas’ adaptability, low price, and lower greenhouse gas emissions from combustion than coal and oil have pushed natural gas demand to rise rapidly, gaining share in all sectors, particularly residential. Residential natural gas usage has risen consistently in OECD countries over time. The amount has increased from around 8 million terajoules (TJ) in 1980 to more than 11 million TJ in 2016. As shown in Figure 1, the total has moved from about 8 million terajoules (TJ) in 1980 to more than 11 million TJ in 2016. Moreover, in order to understand the individual trend of per capita residential natural gas consumption considered as the dependent variable, during the period of study (2005–2016), per capita natural gas demand was plotted in a time-series graph for each country in Figure 2 and note that each country has its own specific trend.
Evolution of aggregate residential consumption for natural gas in OECD countries (terajoules, 1980–2016). Reference: own elaboration based on IEA data.
Evolution of per capita residential consumption for natural gas by country (MWh, 2005–2016). Reference: own elaboration based on IEA data.
These two figures are useful for econometric reasons since they show the evolution of aggregate and per capita individual demand for natural gas in the residential sector over time.
In fact, knowing the numerous factors of residential natural gas consumption and thus estimating the demand equation accurately is critical to creating natural gas legislation and corporate strategies for investors in the natural gas residential sector. To estimate household natural gas demand, the literature has concentrated on the effect of price and income. The majority of these research used static or dynamic models, or both, to simulate natural gas consumption behavior.
In previous energy research ([1, 2, 3, 4, 5]), demographic factors such as the elderly, population density, and urbanization have received little attention, despite the fact that they influence household natural gas consumption explicitly or implicitly. Furthermore, with the exception of Gautam et al. [5], the majority of these research are based on data from before 2010, and it is critical to update studies, especially in this economic field where inputs are rapidly changing.
Policymakers need to know not only how natural gas demand will respond to income and price changes in order to make holistic decisions.
The goal of this research is to estimate the dynamics of per capita residential natural gas demand in 29 OECD nations from 2005 to 2016. The rest of the paper is organized as follows. A brief survey of the literature is presented in Section 2. Section 3 gives a description of the data as well as some descriptive statistics. In Section 4, the estimation findings are shown. A brief conclusion is included in the concluding section.
The impact of population characteristics, particularly age, density, and proportion of urban population, on residential natural gas demand is rare; this could be due to a lack of data. To estimate natural gas demand, most studies focused on price and income elasticities. Several studies, however, have shed light on the impact of population factors on overall household energy usage over the last two decades.
According to several studies, there is a link between household age and space heating energy use. That is, because elderly people are more sensitive to temperature, they consume more energy for space heating than younger people because they spend more time at home. Meanwhile, Chen et al. [6] found that age has a greater impact than wealth in a study of Hangzhou, China, and that there is a negative relationship between occupant age and residential energy usage, particularly for heating and cooling. It was discovered that older housewives are more supportive of economic conduct than younger housewives.
Kronenberg [7] observes in his study on energy consumption and greenhouse gas emissions in Germany that demographic changes, defined by a rise in the number of elderly persons, have a favorable impact on energy demand, particularly for heating.
Liao and Chang [8] used the discrete technique to estimate the space heating and water heating energy demands of senior residents in the United States using data from the 1993 Residential Energy Consumption Survey. They believe that the elderly consumes more natural gas and electricity to heat their homes. However, there is a considerable negative association between water heating energy use and age.
Ota et al. [9] found that the aging of the society has no substantial impact on residential electricity and city gas demand in 47 Japanese prefectures every 5 years between 1990 and 2010. Furthermore, population decline and the rise of nuclear households raise electricity usage while lowering city gas consumption.
Residents who live in densely populated locations (such as Dublin) use less energy for space heating than those who live in less densely populated areas. According to Elnakat et al. [10], a socioeconomic and demographic study on the residential sector in San Antonio, Bexar County, and Texas, areas with higher population density spend less energy per capita than those with lower population density. Furthermore, Arbabi and Mayfield [11] found that for increasing population densities, falling per capita gas consumption patterns are observed in a study aimed at investigating consumption behavior within the transport and home sectors in England and Wales.
Furthermore, He et al. [12] conclude, based on data from 2001 to 2011, that the greater the urban population, the greater the total natural gas consumption.
Rather than calculating natural gas demand price and income elasticities, as most research has done, the focus of this work is on demographic characteristics, particularly the elderly, population density, and urbanization rate.
Between 2005 and 2016, annual data for OECD nations was used. The dataset is based on two main sources: the International Energy Agency (IEA) dataset for residential natural gas consumption and residential natural gas and electricity prices, and the World Bank dataset for per capita income, overall population, population density, urban population percentage, aged population, heating and cooling degree days.
Due to a lack of data, five of the 34 current members of the Organization for Economic Cooperation and Development were excluded from the sample. Due to lacking price data or no reported natural gas demand, Estonia, Iceland, Israel, Norway, and Slovenia were omitted from the list. Figure 3 depicts the study’s precise countries. Every year, each country in the sample is observed, ensuring that the data set is balanced. The following are the descriptive data for the remaining 29 members, as shown in Table 1.
Countries in the study. Reference: own elaboration with tableau public software.
Variable | Label | Mean | Standard deviation | Minimum | Maximum |
---|---|---|---|---|---|
Per capita residential natural gas consumption, MWh | GT | 2.276022 | 1.835515 | 0.036086 | 7.55551 |
Elderly population (total) | ELD | 6,316,611 | 9,100,802 | 67,079 | 4.86e+07 |
Urban population (% of total population) | URB | 77.1913 | 9.726607 | 53.468 | 97.897 |
Population density (Inhabitants/Km2) | DEN | 139.3273 | 128.5861 | 2.654778 | 525.7048 |
Population (total) | POP | 4.23e+07 | 6.10e+07 | 465,158 | 3.23e+08 |
Natural gas end-user price (US$ per MWh) | GP | 75.96573 | 31.68349 | 15 | 169.6404 |
Electricity end-user price (US$ per MWh) | EP | 201.9823 | 67.54572 | 63.73 | 405.56 |
Per capita income (current US $) | INC | 38234.41 | 21450.6 | 7384.258 | 119225.4 |
Annual Heating degree Daysa (baseline: 18°C) | HDD | 3078.315 | 1573.367 | 128.9046 | 8929.906 |
Annual Cooling degree Daysb (baseline: 18°C) | CDD | 252.0716 | 08.8645 | 0 | 1875.273 |
Definition of variables and descriptive statistics. N*T (number of observations × time series) =348.
Heating degree day (HDD) is a quantitative index reflecting demand for energy to heat buildings or businesses.
Cooling degree day (CDD) is a quantitative index reflecting demand for energy to cool buildings or businesses.
The elderly, the urban population, and population density are three crucial variables in the model, taking into account the paper’s demographic methodology. The mid-year population is divided by the land area in square kilometers to get the density.
Figure 4 shows the evolution of the senior population (over 65 years) and the urbanization rate (the fraction of the population living in cities) in OECD countries. The percentage of the population over 65 years old and the population living in urban regions are both growing over time, as seen in Figure 4. These two variables are critical since they account for around 17 and 79% of the total in 2016.
Elderly and population rate (percentage, 2000–2016). Reference: own elaboration based on World Bank data (with tableau public software).
Then, the population density4 of different OECD countries in 2016 was exposed as the year of reference (the most recent one). Figure 5 shows that population density varies dramatically across countries and Korea, Japan, Belgium and Netherlands are the densest countries.
Population density (inhabitants/km2, 2016). Reference: own elaboration based on World Bank data (with tableau public software).
The entire population aged 65 or older in the total population at the national level is included in the empirical model, indicating that each country is aging. The urbanization rate is also included to look into the role of the growing rate of people moving from rural to urban areas. Furthermore, the study considers population density when examining the effects of densely populated countries on residential natural gas use. Other control variables are also included in the model. To determine the income level, the study takes into account the total population (POPit), end-user natural gas price (GPit), and gross domestic product per capita (INCit). In addition, to account for weather effects, the price of electricity (EPit) as the closest replacement, as well as the Heating and Cooling Degree Days (HDDit) and CDDit) were included.
The correlation matrix was used to show the statistical correlation between the dependent variable and the regressors. Table 2 shows that per capita natural gas demand is positively correlated with the fraction of the elderly (ELDit) and population density (DENit) (GTit). The matrix also reveals a negative relationship between urbanization rate (URBit) and natural gas consumption. Furthermore, natural gas usage is inversely connected with its own prices (GPit) and cooling degree days (CDDit), but favorably with per capita income (INCit), population (POPit), electricity price (EPit), and heating degree days (HDDit). The correlation matrix, on the other hand, is a basic statistical link between two variables; as a result, a more precise specification is required to investigate the impact of demographic variables on natural gas demand.
lGT | lELD | lURB | lDEN | lPOP | lGP | lINC | lEP | lHDD | CDD | |
---|---|---|---|---|---|---|---|---|---|---|
GT | 1.0000 | |||||||||
lELD | 0.1264 | 1.0000 | ||||||||
IURB | −0.0953 | 0.0758 | 1.0000 | |||||||
IDEN | 0.3230 | 0.1224 | −0.1979 | 1.0000 | ||||||
IPOP | 0.0840 | 0.9737 | 0.0640 | 0.0751 | 1.0000 | |||||
IGP | −0.0783 | −0.0391 | 0.0245 | 0.4051 | −0.1324 | 1.0000 | ||||
IINC | 0.2804 | −0.1861 | 0.3879 | −0.0331 | −0.3078 | 0.2021 | 1.0000 | |||
IEP | 0.1619 | −0.1635 | −0.0334 | 0.2615 | −0.2900 | 0.6016 | 0.4029 | 1.0000 | ||
IHDD | 0.2240 | −0.1373 | −0.0499 | 0.1075 | −0.2208 | 0.1814 | 0.2796 | 0.0572 | 1.0000 | |
CDD | −0.2327 | 0.2219 | 0.1113 | −0.258 | 0.2995 | −0.3060 | −0.2887 | −0.1846 | −0.8953 | 1.0000 |
Correlation matrix.
Household production theory, which considers the consumer as a firm, assumes that households employ inputs (natural gas in this case) to manufacture nonmarket commodities or utility-yielding items. Thus, the demand for welfare services such as space heating, water heating, cooking, and so on is not directly produced from natural gas. Households use natural gas to meet these demands.
The production function of the welfare services S can be written as:
Natural gas is denoted by the letter G. The quantity of natural gas acquired determines the output, which is welfare services (S). In fact, welfare services S, as well as total consumption X, are regarded to be an element of the household’s utility function. The demographic factors Z and the weather of the household’s country, designated W, have an impact on this utility function. As a result,
The above utility function is maximized by the household under a budget constraint:
Where
Based on the Eq. (4) and employing a log linear specification, The static model is as follows:
Where GTit is per capita residential natural gas consumption, ELDit is the elderly population, URBit is the urban population, DENit is the population density in the country I in year
When estimating a static energy demand model using panel data, the endogeneity problem is often addressed by using the fixed or random effects with the Within estimator or GLS [13], respectively, to avoid the heterogeneity bias with a constant term that the OLS may suffer from.
Nerlove [14], on the other hand, claims that economic behavior models are dynamic in nature, and that current behavior is dependent on the state of the system defining it. Furthermore, according to Gutiérrez [15], disregarding the influence of path-dependency can lead to erroneous estimations of the entire variables. The lagged dependent variable was inserted on the right-hand side of the equation to compensate for the intrinsic dynamic feature of the demand function, assuming that natural gas demand in the residential sector is affected by prior levels. As a result, the dynamic version of the natural gas demand model is:
According to Achen [16], the lagged dependent variable will capture not only the impact of the omitted variables, but also the impact of the variables that have previously been included, with the possibility of modifying or decreasing their impact, sometimes to the point of being inconsequential.
In reality, adding a lagged dependent variable to a static model will result in skewed results because the latter variable may be associated with the error component eit. Thus, the within transformation and GLS will be biased since (
Then using
This section presents the estimated results and their implications on how the demand for natural gas responds to demographic and non-demographic factors in OECD countries. The estimation results for the static model utilizing the fixed effect to adjust for unobserved heterogeneity are shown in Table 3. This indicates that the majority of the coefficients are statistically significant and are nearly identical to previous research findings. In terms of the overall picture, per capita residential demand is statistically significant and positively correlated with urbanization rate, electricity prices, heating and cooling degree days, while elderly, population density, and natural gas prices are negatively correlated with per capita natural gas consumption.
Dependent variable: log residential natural gas consumption per capita | Coefficients |
---|---|
lELD | −0.63*** (0.16) |
IURB | 1.96** (0.68) |
IDEN | −0.31 ** (0.15) |
IPOP | 0.54 (0.45) |
IGP | −0.12** (0.06) |
IINC | 0.07 (0.09) |
IEP | 0.2** (0.08) |
IHDD | 0 .38*** (0.1) |
CDD | 0.01** (0.1) |
Intercept | −10.76* (6.5) |
Sample size | 348 |
0.13 | |
0.06 | |
Overall | 0.05 |
Estimation results: static model—FE.
Significant at the10% level.
Significant at the5% level.
Significant atthe1%level.
Note: Figures in () are the standard error.
However, because static models aren’t ideal for observing economic trends over time, a dynamic panel model was employed to estimate energy demand, which may be more accurate than a static model. In addition, using a lagged dependent variable as a regressor to investigate residential natural gas demand violates the rigorous exogeneity constraint in static models; thus, the lagged dependent variable is included in the explanatory variables in the dynamic model in this work. The dynamic estimating model for residential natural gas demand is shown in Table 4.
Dependent variable: log residential natural gas consumption per capita | Two-step system |
---|---|
Lagged lGT | 0.1* (0.05) |
lELD | −0.61** (0.19) |
IURB | 1.1 * (0 .65) |
IDEN | −0.16* (0.09) |
IPOP | 0.82** (0.26) |
IGP | −0.21*** (0.06) |
IINC | 0.15 (0.10) |
IEP | 0.08 (0.10) |
IHDD | 0.78 *** (0 .11) |
CDD | 0.01 *** (0.01) |
Intercept | −15.89***(3.01) |
Sample size | 319 |
Hansen test | 3.96 (0.79) |
Arellano-Bond test for AR(1) | −1.62 (0.11) |
Arellano-Bond test for AR(2) | −0.71 (0.48) |
Estimation results: dynamic GMM estimation.
Significant at the10% level.
Significant at the5% level.
Significant atthe1%level.
Note: Figures in () are corrected standard error.
The regression results for the dynamic model generated using the two-step system GMM estimator are shown in Table 4. First, the findings suggest that the lagged value of natural gas has a beneficial impact on demand. Furthermore, the calculated results suggest that the elderly coefficient (ELDit) in the residential natural gas demand dynamic equation is significantly negative. That is, as the population ages, natural gas usage declines. The senior elasticity of per capita natural gas demand is estimated to be −0.6. This appears counterintuitive, given that older people are more sensitive to temperature and consume more natural gas to suit their comfort demands, notably for space and water heating, and they spend more time at home [22]. However, possible explanations for the negative effect of ELD on residential natural gas consumption could be related to the fact that either old people in OECD countries have an economic behavior, or that OECD old people do not spend much time inside the house and prefer to do more activities outside. It could also be due to the fact that older folks prefer electric equipment to gas appliances. Another explanation is that, with the recent ubiquity of electrified houses, a major share of appliances used for daily life at home in OECD countries may be electric appliances. The fact that the estimation revealed a negative influence on residential natural gas demand backs up the previous claims.
Second, in the residential natural gas demand dynamic equation, the urbanization rate (URBit) coefficient is notably positive. The elasticity of per capita natural gas demand in relation to urbanization rate, in other words, is expected to be +1.1. The result appears to be consistent with previous research, as natural gas is widely used in cities and less so in rural areas, where coal and wood are commonly used. Coal, wood, and other conventional fuels are being replaced by cleaner energy sources, particularly natural gas and electricity, as the population of rural areas migrates to cities and towns.
Third, when it comes to the influence of population density (DENit), the study reveals that the DENit coefficient is strongly negative. A decrease in per capita natural gas usage occurs when population density rises. The population density elasticity of per capita natural gas demand is estimated to be around −0.16. Residential natural gas consumption per capita is lower in countries with dense populations. A probable explanation for the finding is that the majority of OECD countries apply energy saving processes and choose to use central heating systems, which provide warm space and water to the entire interior of the building.
Almost all of the control variable estimated coefficients are statistically significant, have the predicted sign, and have appropriate magnitudes. Gas prices (GPit) have a negative impact on per capita natural gas demand, whereas population (POPit), heating degree days (HDDit), and cooling degree days (CDDit) have positive impacts on residential natural gas consumption, indicating that natural gas demand is more sensitive to hot than cold weather.
When it comes to assessing the dynamic model, the AR1 and AR2 tests show that there is no significant autocorrelation in the model, which is a need for the instruments’ validity. Furthermore, the Hansen test demonstrates that the null hypothesis, namely, that the over-identifying constraints are valid, is not rejected.
Using a static and dynamic model, this study looked at per capita natural gas demand in the residential sector in OECD countries from 2005 to 2016. The goal of this study is to add to the empirical literature on residential natural gas demand research by analyzing the impact of demographic characteristics on natural gas consumption in the OECD environment, specifically urbanization rate, density, and elderly population.
In fact, no previous study in the OECD has employed a comprehensive model to estimate residential natural gas demand. Previous research has frequently focused on price and income. It is suggested that adding demographic variables will be helpful for policymakers.
A considerable effect of urbanization, density, and elderly population on residential natural gas usage was discovered using a dynamic framework. Due to policy efficiency, rapid urbanization leads to the use of more natural gas per capita, whereas population density leads to the use of less natural gas per capita, especially in buildings. Furthermore, older adults use less natural gas per capita and are more likely to use electric appliances.
Although previous studies have shown that older persons use more energy for heating, these findings appear to be counterintuitive in terms of economic behavior, preference, or the ubiquity of such appliances in OECD buildings.
Supporting women in scientific research and encouraging more women to pursue careers in STEM fields has been an issue on the global agenda for many years. But there is still much to be done. And IntechOpen wants to help.
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\n\nWe aim to publish 100 books in our Women in Science program over the next three years. We are looking for books written, edited, or co-edited by women. Contributing chapters by men are welcome. As always, the quality of the research we publish is paramount.
\n\nAll project proposals go through a two-stage peer review process and are selected based on the following criteria:
\n\nPlus, we want this project to have an impact beyond scientific circles. We will publicize the research in the Women in Science program for a wider general audience through:
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