Average composition of milk and milk powders.
\r\n\t
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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"63169",title:"The Dairy Industry: Process, Monitoring, Standards, and Quality",doi:"10.5772/intechopen.80398",slug:"the-dairy-industry-process-monitoring-standards-and-quality",body:'\nThe implementation of strategies to improve and strengthen milk process optimization is of vital importance within the dairy industry. The rapid deterioration of milk products forces dairy processors to critically optimize and plan their production schedules. The business model is to look at the work force, to reduce or eliminate any time or/and resource wastage, unnecessary costs, bottlenecks, and mistakes while attaining the process objective of creating a quality product [1].
\nThe global dairy sector is currently going through change. The Food and Agricultural Organization of the United Nations (FAO-UN), dairy price index shows prices 26% below its peak from February 2014 [2]. The demand for milk products from China is beginning to slow, trade sanctions on Russia and the end of “milk quotas” within the European Union (EU) has caused a period of excess supply and low prices [3]. Notwithstanding this, the dairy sector is expanding and projected to grow at a rate of 1.8% per year over the next 10 years, to 177 million tons of powdered milk by 2025 [4]. This increase is mainly due to rising urbanization and growing incomes in emerging markets [5]. In the EU, however, dairy farmers have used intervention stocks to shield themselves from poorer international prices. In September 2017, for instance, EU farmers consigned 16,597 tons of skimmed milk powder (SMP) to the interventions stock at Euro €1.698 [6].
\nIn addition, changing consumer demand patterns are affecting food production. The “Traditional” value drivers of price, taste, and convenience have been complemented by newer and “Evolving” drivers such as health and wellness, safety, social impact, and experience. Central to all of these drivers is a need for transparency from food companies [5]. Given the ever-changing nature of the consumer food value drivers, dairy producers must look to their production processes to innovate with new products and to optimize output without compromising on quality and safety.
\nThe world’s milk is predominantly cow’s milk, followed by buffalo milk. The leading producers include, Asia (30%), followed by the EU (28%), North and Central America (18%), South America (9%), other European countries (9%), Africa (5%), and Oceania (5%) [7]. To be named a dairy product, food must be produced from the milk of cows, buffalo, goats, etc. The dairy sector includes food such as liquid milk, milk powders, cheese, butter, and yogurt, as well as ice cream. Several factors including genetics, and breed of animal, environment, stages of lactation, parity, and nutrition, together determine the final composition of milk [8]. Milk and dairy products are significant sources of protein, essential minerals (calcium, potassium, magnesium, phosphorous, sodium, iodine) and several vitamins, (the fat-soluble vitamins A, D, E, K, and B1, B3, B6, B12). In a Western diet, dairy products provide between 40 and 70% of the recommended daily calcium intake. Cow’s milk consists of about 87% water (Table 1), and 12–13% total solids. The solids consist of fat ~4% and solids-not-fat (SNF) ~9%, such as proteins, lactose, and various minerals and vitamins. Milk proteins consist of whey and caseins; caseins have four different species (αS1, αS2, β, and κ-caseins) which are separate molecules, but they do possess similarity in structure and they comprise around 80% of total milk protein. The major whey proteins in cow and sheep’s milk are β-lactoglobulin and α-lactalbumin; the other proteins are serum albumin and immunoglobulins. Minor proteins include lactoferrin (LF), an iron binding protein and β2-Microglobulin—part of the Major Histocompatibility Complex II (MHC II), the rest are mostly enzymes including; lactoperoxidase, an enzyme that breaks down hydrogen peroxide, lysozyme which breaks down bacterial cell walls and has low activity in cow milk, proteases, protease activators, nucleases, glycosidases, and others. The milk proteins contain the nine essential amino acids required by humans, making it an important human food. The caseins are easily digested, while the whey proteins are relatively less digestible in the intestine.
\n\n | Cow’s milk % | \nSkim milk powder (SMP) % | \nWhole milk powder (WMP) % | \nAcid whey powder (WP) % | \n
---|---|---|---|---|
Moisture | \n85.5–89.5 | \n3.0–4.0 | \n2.0–4.5* | \n3.5–5.0 | \n
Fat | \n2.5–6.0 | \n0.6–1.5 | \n26.0–42.0 | \n1.0–1.5 | \n
Protein | \n2.9–5.0 | \n34.0–37.00 | \n24.5–27 | \n11.0–14.5 | \n
Lactose | \n3.6–5.5 | \n49.5–52.0 | \n36.0–38.5 | \n63.0–75.0 | \n
Minerals (ash) | \n0.8–0.9 | \n8.2–8.6 | \n5.5–6.5 | \n8.2–8.8 | \n
The milk fat content varies within the same dairy products and between different dairy products. Raw farm milk, full-fat milk, semi-skimmed milk, and skimmed milk have their own percentage of fat. Raw milk normally has a fat content of ~4.4 g of milk fat per 100 g. This can be skimmed to obtain lower fat varieties. Full-fat milk is standardized to 3.5% of fat and semi-skimmed milk contains ~1.5% fat. Skimmed milk and buttermilk are very low in fat and, on average, contain 0.1 or 0.2% fat, respectively. The fat content of milk and cream is also known as butterfat, an important factor in determining the price to be paid for milk supplied by farmers in many countries. Milk sold to the consumer is standardized with a range of different fat content choices. However, international variances in standardization mean that the fat percentage for (semi)-skimmed, whole milk, and buttermilk can differ between countries. Modifications in the composition of milk are allowed, if they are indicated on the packing of the product, so that it can be easily seen and read, complying with the obligation as regards nutrition labeling, laid down by the countries regulations. In the case of the EU, regulation No. 1169/2011 applies on the provision of food information to consumers [9], plus providing an indication of origin, is considered of particular interest. The US Public Health Service (USPHS) Milk Ordinance and Code recommends a minimum of 3.25% butterfat in farm milk, as the official national standard [10].
\nMilk is not necessarily a local product and has developed into a global trade with the development of milk powders. In particular, whole milk powder (WMP) and skimmed milk powder (SMP) are the most traded agricultural commodities globally, as percentage of production traded, while fresh dairy products, with less than 1% of production traded are the least traded agricultural commodity [4]. The dairy industry, however, has been targeted in the climate change debate as it has been estimated that 14.5% of greenhouse gas emissions come from livestock with beef and milk production the main culprits [13, 14]. Extreme changes in climate can affect the microbiological safety of food. Wet conditions are favorable to pathogen growth and may result in increased risk of food contamination, including mycotoxin. Aflatoxin M1 is the most studied mycotoxin in milk and levels exceeding the EU maximum level (0.050 μg/kg) have been found [15]. Climate effects on animal diseases lead to increased use of veterinary medicines resulting in drug resistance and anthropogenic (synthetic) chemicals with the potential for transmission of chemical residues into the food chain. The more frequent and intense rainfall that is predicted could encourage the spread of perchlorate through surface runoff with the potential to enter the food chain via cow’s milk [16]. Perchlorate reduces thyroid hormone production in the thyroid gland [17].
\nThe flow diagram for milk processing is presented in Figure 1. Milk arrives at the milk dairy processing plant over the weighbridge and the weight of milk is automatically recorded. At the same time, data from an on-board computer is downloaded wirelessly to a data capture system, which holds the records of the temperature and volumes of milk collected from each farm. The temperature should be at 4–6°C. Milk samples using sterile containers are collected automatically from each supplier at source and are delivered to a laboratory technician for detailed analysis. Milk that deviates in composition, taste, and smell from normal milk receives a lower quality rating. The technician also takes a composite sample, from each compartment in the refrigerated truck, which is compartmentalized to reduce sloshing of the milk. The samples from each compartment are tested for acidity, antibiotics, added water, fat, and protein content. These analytical tests and methods are determined by international standards as outlined in Table 2.
\nMilk processing stages.
Quality tests | \nAcceptable limits | \nStandards | \nReference number | \n
---|---|---|---|
Acidity (Titratable) | \n≤0.18% | \nISO 6091:2010 | \n[19] | \n
Antibiotic residues | \nAbsent/0.1 g | \nISO 26844:2006c | \n[20] | \n
Freezing point (added water) | \n−0.54°C | \nISO 5764:2009 | \n[21] | \n
Fat | \n0.8% | \nISO 1736:2008 | \n[22] | \n
Protein | \n34% | \nISO 8968–1/2:2014 and ISO 14891:2002 | \n[23, 24] | \n
Lactose | \n>4.2% | \nISO 22662:2007 | \n[25] | \n
Quality analytical tests for raw milk.
The ISO standards catalog ISO/TC34/SC5 [18] lists all milk and milk products standards, while other standard sets include, microbiology of the food chain, microbiological quality of milk, etc. The bacterial quality of the milk is also measured and these specify tests are outlined later.
\nThe titratable acid test measures the acidity of the milk. Both titratable acidity (TA) and pH are measures of acid. TA is a more reliable indicator because relative to pH measurement, it is more sensitive to small changes in milk acidity, especially important in cheese making. The acidity of milk is of two types; natural acidity due to citrates and phosphates present in the milk and dissolved CO2 during the processing of milking. The second is the developed acidity due to lactic acid produced by bacteria using the lactose in the milk as a nutrient, converting it to lactic acid. The acidity of milk measures the total acidity (natural acidity of milk and developed acidity). The International Standard Method for titratable acid is ISO 6091:2010 [19]. Titratable acidity is a measure of the buffering of milk between pH 6.6 and 8.3 (phenolphthalein endpoint) [26]. The appearance of a faint pink color, which signals the endpoint and the number of ml of NaOH used to reach the endpoint, is recorded. This value is called the “titer,” titratable acidity is reported as percent lactic acid and is dependent on the volume of sample. As this test is dependent on the analyst reading eye measurement of the color change, it is prone to human error causing incorrect and unpredictable recording of results.
\nThe antibiotic test uses kits known as Charm and Delvo tests. The Charm test is made by Charm Science Inc., e.g., one kit, the Charm Rosa TET–SL (
Added water can be measured by changes in the freezing point of milk from its normal values, the current official freezing point limit is −0.525° Horvet or −0.505°C and was designed for whole-herd, bulk-tank samples, or processed milk samples. The freezing point of milk is the constant physical-chemical property of milk, which is determined only by its water-soluble components such as lactose, and salts, which in accordance with the Wigner law are held in milk at an approximately constant concentration. However, the mineral composition of milk depends on lactation, nutritional status of the animal, and environmental and genetic factors [30].
\nAdulteration of milk with water will cause a measureable rise of the freezing point of milk. The freezing point is also lowered by acidification of milk, which leads to protein denaturation. The freezing point is considered as an accurate and sensitive method, most laboratories use a cryoscopy, method that is the ISO reference method ISO 5764:2009 [21].
\nThe average fat content of raw milk is ~4.4 g of milk fat per 100 g; with more than 400 various fatty acids (FA) being present in milk [31]. The milk fatty acids are derived almost equally from two sources, the feed and the microbial activity in the rumen of the cow [32]. A study of Swedish bovine milk found that the milk contained substantial quantities of unsaturated fatty acids with 4–10 carbon chains (C4:0–C10:0), about 2% each of saturated C18:2 and trans-C18:1, and almost no other long-chain polyunsaturated fatty acids. The most important fatty acid from a quantitative viewpoint was palmitic acid (C16:0), which accounted for approximately 30% by weight of the total fatty acids. Myristic acid (C14:0) and stearic acid (C18:0), made up 11 and 12% by weight, respectively [31]. Fatty acid composition can show rapid and significant variation in response to changes in diet. The ISO standard for fat determination is ISO 1736:2008 [22].
\nThe fatty acid make-up of the milk can be altered by changes in diet [33], but are also affected by a number of factors, including diet composition, nutrient utilization, lactation cycle, breed of cows, with dietary variations changes up to 3% units, been reported [34]. Specific fatty acids produced during microbial fermentation of dietary fats in the rumen of the cows are responsible for low milk fat. 2–3 g of these fatty acids can decrease milk fat by 0.5% or more [35]. The Gerber method is a historic method still used today to find the fat content of milk in particular in milk powders. By using specific butyrometers designed especially for the different dairy products, e.g., for cream, ice cream, whole milk, or cheese butyrometers, with method modifications [36]. There are many suppliers of such analytical tools, e.g., Gerber instruments (
The protein fat and lactose content of milk has a bearing on the price the farmer achieves for its milk. Liquid milk contains around 3.4% protein. The proteins in milk were described previously. The determination of protein content of milk and milk products underpins the international trade in dairy products. There are different analytical approaches for the determination of protein quality for nutrition purposes and chemically defined protein. These are divided into three broad categories: (i) determination of total nitrogen, (ii) direct protein determination, and (iii) indirect protein determination [40]. The Kjeldahl method ISO 8968/1:2014 [23] and Dumas method ISO 14891:2002 [24] are the current international standards, and use chemical digestion and combustion approaches respectfully. The advantage of these methods is that they have high reliability and accuracy. Using these methods, around 95% of nitrogen in milk is found to be present as proteins, with the remainder as nonprotein nitrogen sources such as urea. Together these tests and values form the basis for testing the quality of milk and milk products.
\nThe raw milk in the milk container truck, having passed the preliminary analytical tests, proceeds to whole milk intake bays and the milk hoses are connected up by the driver. The milk is pumped into bulk storage tanks called milk silos (capacity can be up to 300,000 l, plus). The driver enters the trucks identification number on the pump’s control panel or uses a key fob (a passive wireless electronic device that usually uses radio frequency ID technology) to start pumping into the whole milk silos. Unloaded milk is cooled automatically to 4–6°C with a heat plate exchanger (HPE) while pumped into the silo. The offload time and setup time taken to couple and decouple the milk intake hoses are areas where processing monitoring can be implemented. The pumping time can be variable, indicating performance specific to each pump and the flow rate represents a reasonable performance indicator. Other significant factors that can influence pumping time include the volume of milk in the receiving silo, the number of bends and valves in each pipeline, and the associated backpressure variations. At milk offload, process optimization can be achieved by ensuring pumps are working effectively, efficiently, and planning truck supply due to intelligent time slot management.
\nDifferent milk processing plants have their own process trains. In many cases, milk must be clarified on reception at the dairy, to remove particles of dirt such as sand, soil, dust, and precipitated protein, which will protect downstream processing equipment. In addition, removal of bacteria, spores, and somatic cells from milk can be achieved with centrifugation and microfiltration techniques [41]. Somatic cells such as leucocytes are removed, which will reduce the presence of Listeria trapped inside the leucocyte [42]. Reduction in the microbial load at this point can decrease the burden of biofilms [43], which leads to more efficient work of the HPE [44]. Milk bacterial clarification also avoids problems during cheese aging, and improves shelf life and organoleptic properties of the dairy products. A clarifier is a type of centrifugal separator, but clarifiers and milk separators serve slightly different duties. All centrifuges can act as clarifiers; however, in general, only centrifuges with a high hydraulic capacity are used in this way. The clarifier can function with either cold (below 8°C) or hot milk (50–60°C).
\nThe main use for centrifuges in diary processing plants is hot milk separation. The aim is to separate the globular milk fat from the serum, the skim milk. This process is known as skimming. This process is generally combined into the pasteurization line and joined with an in-line fat standardization system for both milk and cream. Separation normally takes place at 122–140°F (50–60°C). The fat content of the cream discharged from the separator can be controlled to a level of between 20 and 70%. The terminology for separation in the dairy industry includes continuous centrifugal separation of solid particles (Clarifier), separation of cream (Separator), or separation of bacteria (Bactofuge). The microbial quality of milk powders is highly significant and it is possible at this early phase of processing to remove 99.9% of the spore-forming bacteria by either bacto-fugation or microfiltration preceding heat treatment.
\nStandardization of milk is the alteration of fat and solids-not-fat (SNF) levels, i.e., raising or lowering of these levels. This is regularly carried out for the consumer market milk supply and in the production of other milk products including: condensed milk, milk powder, ice cream and cheese, etc. Standardization is typically carried out to create a uniform milk fat content in the final dairy product [45].
\nPasteurization was originally introduced to control Mycobacterium bovis, which causes tuberculosis (TB), which is no longer problematic as cows are tested for TB annually and removed from herds if they test positive for the disease [46]. The TB bacillus is a highly heat resistant microorganism; however, Coxiella burnetii, the cause of Q fever in humans [47], required pasteurization of 161°F (71.7°C) for 15 s, and is the current official standard for milk pasteurization [48], the standard vat pasteurization is 63°C (145°F) for 30 min. However, heat processing can result in the loss of subtle aroma and flavors components, loss of vitamins and natural antioxidants, the loss of texture and freshness, and the denaturation of proteins. The US Grade A pasteurization milk ordinance (PMO) is managed by the Departments of Health and Human Services and Public Health, and the Food and Drug Administration and gives the criteria concerning the milk parlor and processing plant design, milking practices, milk handling, sanitation, and standards for the pasteurization of Grade A milk products. Regulation of milk processing is controlled on each US state basis; however, all dairy products must meet the regulations outlined in the PMO for products that will be sold outside of that state [10].
\nThe center for disease control (CDC) in the US, reported that unpasteurized milk is 150 times more likely to cause foodborne illness and results in 13 times more hospitalizations than illnesses involving pasteurized dairy products [49]. The dangerous bacteria include Salmonella spp., Escherichia coli, and Listeria monocytogenes; it is also for this reason milk is pasteurized. E coli 0157 emerged in 1982, while multidrug-resistant Salmonella typhimurium DT104 was reported in 1990 [50, 51] with some E. coli and Salmonella isolates resistant to seven antibiotics. The EU Center for Disease Control and Infection (ECDC) reported Listeriosis cases of 2536 in 2016, of which L. monocytogenes was most frequently detected in both soft and semi-soft cheeses prepared from raw milk (2.5%), while 0.7% of raw milk (n = 968) samples tested positive [52]. These pathogens can also be found in multiple food products including meat [53]. However, postpasteurization contamination has been found to be the most causative factor in microbial outbreaks due to milk products [54, 55].
\nThe PasLite test is an internationally accepted method used by dairies and food manufacturers to verify pasteurization for many types of dairy products. The PasLite test verifies the completeness of milk pasteurization by detecting alkaline phosphatase, a natural enzyme in milk that is destroyed by the heat and hold time of pasteurization. The test takes 3 min and multiple samples can be run simultaneously, however only one sample can be read at a time [56].
\nWhen a dairy sample is mixed with PasLite reagents and incubated, the resulting solution emits light in an amount directly proportional to the phosphatase enzyme present. The Charm nova LUM ATP detection system is used to measure the light emitted and coverts light readings to enzyme units. Phosphatase readings greater than 350 mU/L indicate product pasteurization issues, according to US and EU pasteurization requirements. The PasLite test detection limit for liquid dairy products is 20 milliunits per liter (mU/L) phosphatase (~0.002% raw milk). This is much lower than the 350 mU/L level (0.1% raw milk) mandated by nearly all public health agencies.
\nThe development of milk powders has revolutionized the dairy industry and allowed for a highly nutritional foodstuff to be exported safely around the world. Milk contains 85–90% water (Table 1); it is reduced by removing the water and can reduce the milk weight to 12% w/v, allowed for cheaper and easier transport. History tells us that in the thirteenth century, Marco Polo reported that soldiers of Kublai Khan carried sun-dried milk on their expeditions [57]. In 2013, the world’s largest dairy spray dryer was installed by Fonterra Dairy Co-Op in New Zealand that has a capacity to produce 30 tons of milk powder per hour, converting four and a half million liters of fresh milk each day [58]. Three years later, in 2016, a “second of its kind,” world’s largest spray dryer started production at another Fonterra milk powder plant, which illustrates that, the trend is toward maximized production of dairy powders.
\nMilk powders can include whole milk powder (WMP), skim milk powder (SMP), fat filled milk powder (FFMP), infant formula, and milk protein concentrate, which is 85% pure milk protein. Its uses include in bakery, confectionary, ice cream, and in fermented food such as yogurt. Many are advertised as nutritional supplements and are fortified with vitamins, folic acid, and iron.
\nMilk powder is manufactured by spray-drying precondensed milk. A falling film evaporator is commonly used in the dairy industry to concentrate the milk from ~13% total solids (TS) to a target of up to 52%. Evaporation is simply the removal of a solvent from a solution or slurry. Milk itself is defined as a colloid with the solvent being the water. Other methods of removing water can include freeze-drying [59]. The constituents of milk can be seen in Table 1. As some products are sensitive to heat, the design of evaporators with respect to temperature and holding time is vital in order to achieve the desired effects on the one hand, but without causing heat damage and denaturation to the milk proteins. To minimize the thermal impact on the products from the heat applied, evaporation takes place in a vacuum at pressures of 160–320 hPa, equivalent to water boiling temperatures of 55–70°C. Energy efficiency is the main driving force in improved design and technologies in evaporation [60, 61]. Inside the evaporator are a bundle of tubes for the exchange of heat and these are enclosed in another steel cylinder, in evaporation parlance called a “calandria.” The vaporized solvent is cooled to condensate, which is then removed. It can go to storage, be recirculated, recovered for heat transfer, or filtrated but this is secondary to the evaporation process itself. The main unit of an evaporator is called an “effect.” Generally, more than one “effect” is used, to increase efficiency by using the heat from the vapor from the previous “effect” to heat the feed in the next. Steam economy is a term used to quantify how much original steam is used in ratio to vapor steam. If 1 Kg of steam produces 1 Kg of vapor in a single “effect” system, 1 Kg of steam will produce ~2 Kg of vapor in a two “effect” system. The specific steam consumption of the former is 100%, while it is 50% in the latter case. This cuts down the cost of generating original steam feed. A subsequent “effect” must have a lower pressure than the previous “effect,” and a step-wise vacuum is applied to the whole evaporation process to achieve this. There are three main elements in evaporation: heat transfer, vapor-liquid separation, and energy efficiency [62].
\nWhen milk leaves the evaporator (Figure 1), it is passed through the spray dryer through small nozzles, which make small droplets or atomizing the liquid, the smaller the better. The drying chamber has a temperature of 160–205°C, the droplets are swirled around (1 l of concentrate is atomized to 1.2 × 1011 droplets with a diameter of 50 micron with a total surface of 120 m2.). For effective drying, the air should be hot, dry (low humidity) and moving. The powder falls to the bottom where it is collected in a “fluid” bed under the cone of the drying chamber, where fine powder behave in an analogous manner to a liquid and it can be conveyed without forming clusters. Fluid beds permit mild second stage drying and cooling of delicate products. Agglomeration changes the bulk density of the product [12].
\nThe bulk density of the powder can dictate how the milk powder dissolves in hot beverages including for tea, coffee, and chocolate. The particle size of the milk powders determines its reconstitution properties. Powders consisting of particles of <100 μm are difficult to wet with water and form lumps [63], in the case of full-fat milk powder (FFMP), which is difficult to wet, it is sprayed with lecithin or oils (e.g., palm) to improve reconstitution characteristics. The standard method for measuring bulk density is ISO 8966:2005 [64].
\nMilk powders can be classified accordingly to the heat treatment they receive. There are five levels of heat classification: ultra-low (<70°C/15 s) common low (70°C/15 s), medium (85–90°C/20–30 s), high (110–135°C/30 s), and high-heat stable (~135°C/30 s). The whey protein nitrogen index (WPNI) expresses the content of un-denatured whey protein (mg WPNI per gram of powder) and demonstrates the severity of the heat treatment. Low-heat WPNI >6.0 mg, while high-heat WPNI is <1.5 mg, values are expected [65]. An alternative heat classification of milk powder is by casein number (CN—total nitrogen precipitated at pH 4.7), this measure was introduced as the protein concentration in milk changes with the seasons and feeding patterns [66]. The CN number is not linked to the overall protein content of the milk. The CN value of high quality raw milk is in the range 80–82, expressed in percentages. The CN values in excess of 82 indicate that the denaturation of whey protein has taken place. Completely denatured milk has a CN value of 92.
\nThe composition of and additives allowed in milk powders are regulated by the Codex Alimentarius Commission—Milk and Milk Products [67, 68]. The Codex standard stipulates that only milk and cream may be allowed in milk powders; though the protein content can be altered by adding lactose. Milk proteins include casein complexes and whey protein fractions. Casein is the most abundant with whey proteins in lower concentrations. The casein concentration in cow milk is 2.46–2.80/100 g and whey proteins in the range 0.55–0.70/100 g. The composition of milks from various animal species is well reviewed in Barłowska et al. [69].
\nThe moisture content of milk powder must be controlled during milk processing, as it is a factor in the long-term quality of the product, and it influences the cost of production. The method for determination of the moisture content includes the ISO 5537: 2004 reference method [70] and IDF Provisional Standard 26A:1993 [71] and EU commission Directive method (79/1067) [72]. A test portion of milk powder is dried at 102 ± 2°C until constant mass is obtained, but this measurement can be affected by the relative humidity of the air in the laboratory where the test is carried out. Rapid methods and newly designed equipment are always being introduced to avoid air humidity interference in the measurement and one new method is by using a microwave cavity perturbation technique [73].
\nEach step along the milk processing train can be contaminate by the air [74] and the water [75], used in the milk processing stages. Hygiene control at all stages, including hygienic design of the manufacturing equipment, is critically important.
\nThe microbial quality of milk starts a farm level. Milk is sterile at secretion in the udder but is colonized by bacteria before it leaves the udder [76]. The temperature of milk expelled from the udder is approximately 35°C; to prevent microbial growth, rapid cooling, and storage to 4°C is necessary. The dairy farmer has the responsibility of managing and maintaining a clean and hygienic milking parlor with a good milking and storage routine. The farmer can detect early signs of mastitis infection by using a somatic cell count (SSC) test. Low levels of SCC (<200,000/ml) are wanted to guarantee good extraction of protein from milk. High levels of SCC also reduce other levels of milk constituent including lactose. The California Mastitis Test (CMT) offers a quick and easy on-farm test; the test does not provide a specific SCC, but will give a positive result once a cow’s SCC goes over 400,000 cells/mL. The addition of the CMT solution to milk samples with a high number of leukocytes/white blood cells causes the solution to become mucous like. This reaction is caused by the release of DNA from somatic cells, which are now higher due to the immune response of the cow to infections. Mastitis is caused by the microorganism Staphylococcus aureus. CMT test are available commercially from many companies.
\nThe milk tanker driver can perform a few tests at the farm, but this is not often practical. The collector will also take a sample of raw milk and label it with a bar code identifier, to be brought back to the dairy processing plant. Composite samples are taken for the detection of inhibitory substances (e.g., antibiotics, antiseptics) to be tested later at the processing plant and if positive the individual suppliers samples are then analyzed.
\nAt the milk intake point, the milk is tested before acceptance into the processing train. One such historic test described in 1929 [77, 78] is the Resazurin test, which determines the microbiological quality of the milk. The theory of this test is that Resazurin, a blue dye, is reduced in an oxidation-reduction reaction, as bacteria grow in the milk they use up oxygen and this can reduce the Resazurin dye to a pink color. All that is required is 10 ml of milk, 1 ml of resazurin solution (0.05%), mix well and incubate at 37°C for 2 min. The color changes from blue to mauve to purple to pink and lastly colorless and is compared to standardized color disks or measured in an instrument called a Comparator (developed by Lovibond, originally) which is a short path length instrument (up to 40 mm) for visually matching samples with relatively dark colors. A reading of ≥4, which is comparable to an estimate of a total bacterial count of 0.1–2 million cfu/ml, is a satisfactory milk quality result.
\nThe milk density is another rapid test to determine adulteration of the milk and an indication for the deviations from the normal milk composition, for example, if it has been watered down or skimmed. In this test, a dipping lactodensimeter combined with a thermometer is used (Gerber instruments; Brouwland instruments), lactometers/milk hydrometers are calibrated in either grams per milliliter (g/cm3), degrees specific gravity (SG), or Degrees Quevenne. 1° Quevenne = 0.001°SG. Density ranges for standard milk are between 1.026 and 1.034 g/cm3. The adding of 10% water to milk will end up decreasing milk density by ~0.003 g/cm3.
\nA wide variety of bacteria grow and survive in milk, including problematic spore-forming bacteria [79] and pathogens such as nontyphoid Salmonella, Campylobacter, Listeria monocytogenes, and Shiga toxin-producing Escherichia coli are also found [80]. In addition, Cronobacter sakazakii has been found in milk powder producing plants and is a particular risk to infants [81].
\nThe common bacteria in milk are lactic acid bacteria (LAB), which can produce enough acid to reduce the pH of milk, and cause the coagulation of proteins, thus fermenting the milk [82]. The density test as previously described should be introduced at milk intake, as it can determine the degree of LAB growth. LAB can be divided into rods (Lactobacillus and Carnobacterium) and cocci (all other genera).
\nPsychrotrophic microorganisms are also present up to 80% in fresh collected milk, they are able to grow quickly below 7°C, and some contain heat-stable enzymes, which cause spoilage, including many Gram-negative bacteria, such as Pseudomonas fluorescens, Pseudomonas fragi, Pseudomonas putida, Achromobacter, Aeromonas, Alcaligenes, Chromobacterium, Flavobacterium, Serratia, and Enterobacter [83].
\nThermoduric bacteria can survive pasteurization. They do this by forming spores, which can then carry over into the final product. This can cause quality defects in milk products such as decreasing the shelf life of pasteurized milk. They are represented mainly by Gram-positive bacteria, e.g., Bacillus and Clostridium spp., and the nonspore-forming genera, e.g., Micrococcus, Streptococcus, and Corynebacterium. Levels of greater than 1000 cfu/ml are normally the result of poor cow hygiene and milking equipment (particularly in the case of ineffective hot wash routines). Potential sources of thermoduric bacteria include silage, faces, animal bedding, and soil [84]. Thermophilic bacteria grow in milk held at raised temperatures (55°C or higher), including pasteurization, 62.8°C, they include the Bacillus spp. Thermophilic bacteria are monitored by standard plate count methods with incubation at 55°C [85]. However, obligate thermophiles, such as Geobacillus stearothermophilus and Anoxybacillus flavithermus tend to grow to high numbers in milk powder manufacturing plants [86]. Although these microorganisms generally are not pathogenic, there is evidence to show that they cause human diseases [87], their growth results in high bacterial numbers and their presence can be interpreted as an indicator of poor plant hygiene. Spores of G. stearothermophilus are also able to survive ultra-high-temperature (UHT; 134–145°C for 1–10 s) treatment [88]. A recent study outlined the prevalence of contaminated milk processing samples with spore-forming bacteria, which increased from 23% on farm, to up to 58% post pasteurization stage [89].
\nThe total viable count (TVC), or total bacterial count (TBC), is used to indicate the overall level of microorganism in milk; E. coli and coliforms to indicate any fecal contamination; and Pseudomonas spp., to indicate any nonfecal contamination. EU legislation, describing precise hygiene rules for foods from animal origins (amended in 2017) lays down comprehensive criteria for milk quality [90]. The ruling indicates that TBC in raw milk should be less than 100,000 cfu/ml; however, a TBC of less than 15,000 cfu/ml is desired. A standard to aim for is <1000 cfu/ml as milk leaves the udder; <3000 cfu/ml as milk leaves the milking machine; and <5000 cfu/ml in the bulk tank. Further contamination takes place during storage and preprocessing activities.
\nNew technologies in the dairy industry are slowly integrating both at farm level and in the dairy processing plant. At farm level, the introduction of robotics such as automated milking parlors developed by Lely and introduced in 1992 by Delaval (Sweden). The cows enter the parlor without prompting and some cows are milked three times a day, with increased milk product for the farmer. The tags on the cows allow for integration into the machines which collect vast amounts of data, including number of steps, chewing the curd, etc. Robotic milking machines have a life span of approx. 13 years and then required further investment. Determination of when a cow is in heat for efficient reproduction is available with MooCow developed by Dairy Master (Ireland), together with MooMonitor to guide cows in the parlor. A separate company created MooCall, a sensor attached to the cow tail, which can monitor contractions during calf birth and send a SMS message to the farmer, the sensor can determine as close as 1 h to delivery [91]. Some of the more recent analytical instruments for milk analysis that has been introduced, but are not yet standard and include: Fourier transform MIR spectroscopy for milk-based quantitative, qualitative phenotypic and genomic analysis. Flow CYTOMETRY is a well-established technique for bacteria and somatic cells counting and differentiation [92], and companies making these include: Bentley (
The milk processing chain demands accurate and quality products from farm to plate and for all of its products, e.g., fluid milk, milk powders, etc. It must start with the raw material at farm level including; dairy herd improvement testing, to payment parameters, and quality control of the raw milk. Optimization is important in the processing of milk in the dairy chain as 73 plus tests are carried out including chemical physical and microbiological tests, set against ISO standards, EU, USFDA regulations, and most countries internal regulations. Advances are slowly being made to have modern and optimized methodologies approved. The regulatory bodies are setting new standards from verified inter-laboratory studies, targeting the advancement in instrumentation and for at-line and in-line production analysis for improved predictability and control of manufacturing processes. The finished product must be safe and comply with regulatory requirements.
\nAt a conference in Glasgow (Semex Dairy Conference, Jan 2018), it was questioned whether the dairy industry could cease to exist after approximately 10 years, due to the interest in vegan alternatives and the increased population who are lactose intolerant [99, 100]. A business model to address this alternative has resulted in a cow-free milk product called Perfect Day, an animal-free milk made by using yeast and fermentation techniques to produce a product with equivalent dairy proteins (
This work was supported by the Irish State through funding from the Technology Centre’s program—Grant Number TC/2014/0016.
\nThere is no conflict of interest.
Technological modernization is one of the key elements of success in improving productivity and environmental management [1]. It has advanced in power plants and as a result, energy efficiency has increased. Regarding plant efficiency, there are numerous findings from the engineering viewpoint [2, 3]. Research on fuel cells is also active, Taner [4] measuring the energy efficiency of the proton exchange membrane fuel cell. Based on these technical studies, this chapter focuses on the efficiency of the overall energy demand in a country and region, not the individual efficiencies of plants and technology unit. In other words, this study analyzes the energy consumption efficiency from an economic viewpoint. Energy consumption is primary and secondary, or final energy consumption. The focus of this study is final energy demand efficiency.
Energy consumption is mainly affected by energy efficiency. Given the current trend in Japan, the energy saving in the manufacturing sector as a subsector of the industrial sector has strengthened, given the drastic improvements in the energy efficiency of factory facilities. However, in the commercial sector as another subsector of the industrial sector, energy saving has deteriorated and this, in turn, has increased energy consumption. Japan’s industrial sector accounts for a large proportion of the nation’s energy consumption, and thus, increasing the energy efficiency of this sector has become a key policy issue.
There is no clear and accepted definition of energy efficiency, but according to Bhattacharyya [5], most definitions are based on a simple ratio of “useful output of a process/energy input into a process.” Additionally, Patterson [6] shows several ways to quantify the output and input of this ratio. One of the ratios most frequently used in energy analysis at the macro level is the energy-GDP ratio, called energy intensity, which is in fact the reciprocal of the economic-thermodynamic index of energy efficiency identified by Patterson [6]. Energy intensity has been traditionally used as an indicator of energy efficiency. However, this approach has been disputed by the claims that energy intensity may not reflect the specific factors that enable energy intensity to accurately approximate energy efficiency [7, 8, 9]. An Energy Information Administration (EIA) [7] report first highlighted that energy intensity and efficiency are often used interchangeably and discussed the use of energy intensity as a measure of energy efficiency. Energy intensity is thus susceptible to socioeconomic factors other than energy efficiency, such as energy price, income, and production environment. Given this energy intensity problem, we need to control other important factors to obtain a pure measure of energy efficiency. Therefore, numerous studies attempted to measure the energy efficiency indices by conducting stochastic frontier analysis (SFA) and data envelopment analysis (DEA).
For instance, Huntington [10] discusses the relationship between energy and production efficiency using the framework of production theory. Feijoo et al. [11] conduct SFA to measure the energy efficiency of Spanish industries and Buck and Young [12] to estimate the energy efficiency of commercial buildings in Canada. Similarly, Boyd [13] analyzes the energy efficiency of wet corn milling plants and highlights the advantage of not having to define the problem of energy intensity in an SFA. Further, Zhou and Ang [14] measure the energy efficiency of 21 OECD countries using DEA. On the other hand, Filippini and Hunt measure the energy efficiency of 29 OECD countries [15] and calculate the energy efficiency of the US household sector using SFA [16]. The authors show that the energy efficiency level measured by conducting an SFA is not correlated with energy intensity, thus concluding that energy intensity is not a suitable proxy for energy efficiency. Carvalho [17] follows a time frame similar to that of Filippini and Hunt [15] and covers a series of non-OECD countries. Aranda-Uson et al. [18] perform an SFA to measure the energy efficiency for Spain’s grocery and tobacco manufacturing, textile, chemical, and nonferrous metal product manufacturing industries. China-based studies have also applied SFA to measure the energy efficiency of the thermal power [19], iron and steel, and chemical industries [20, 21]. Lin and Du [22] and Filippini and Lin [23] compare energy efficiency levels across Chinese provinces using various econometric models, including SFA.
In sum, numerous studies support the use of an SFA instead of energy intensity as an indicator of energy efficiency. Moreover, SFA is a parametric approach that can tackle statistical noise and thus, is more desirable than DEA, a nonparametric approach. To this effect, Zhou et al. [24] evaluate the energy efficiency index using both approaches and show SFA is more desirable than DEA. A large body of research focuses on measuring energy efficiency values using SFA, whereas few studies explore the individual factors determining energy efficiency levels, such as the empirical works by Otsuka [25, 26]. These studies analyze the energy consumption trends of households and reveal that resident characteristics determine energy and electricity efficiency. However, to the best of the author’s knowledge, there is a scarcity of research on economic production sectors. Particularly, how mechanization and electrification affect the energy efficiency have not been clarified.
This study thus measures the level of energy efficiency by using SFA and clarifies the determinants of the improvements in energy efficiency for Japan’s industrial sector. Specifically, it focuses on two factors influencing the energy efficiency of the industrial sector. The first is the capital-labor ratio, that is, “mechanization,” wherein installing large intensive machinery equipment deteriorates energy efficiency. Conversely, the installation of compact and dispersed production facilities is expected to increase the energy efficiency. The second factor is the electrification rate. Advancing the electrification of factories and offices is directly linked to greater operational productivity and thus, the possibility of increasing energy efficiency.
Porter and van der Linde [27] highlight that improving productivity throughout the production process under appropriate environmental regulations could relatively reduce energy usage and, consequently, increase the energy efficiency. Boyd and Pang [28] and Otsuka et al. [29] also empirically demonstrate that productivity gain improves energy efficiency, that is, energy efficiency serves as a guidepost for improving productivity. Drawing on these works, this study verifies the hypothesis that productivity improvements under environmental constraints are compatible with those in energy efficiency.
The remainder of this study is organized as follows. Section 2 describes the empirical analysis framework, as well as the models and data. Section 3 presents the empirical results, followed by an analysis of the findings. Section 4 concludes the study.
This study assumes the following aggregated energy demand function, f, exists at the Japanese prefectural level. That is,
where j denotes a region (j = 1, …, J), t is time (t = 1, …, T) and E is the final energy consumption for the industrial sector. P is the energy price index for the sector and Y income. KL is the capital-labor ratio and represents the degree of mechanization in a factory or office. Thompson and Taylor [30] show that capital and energy both have short- and long-term relationships. IK is the proportion of investment in capital stock and represents the degree of vintage. CDD and HDD are the cooling and heating degree days and represent temperature. In regions with severe temperatures, energy consumption is more likely to be associated with air conditioning. Previous studies have shown that CDD and HDD, as indicators of cooling and heating, are related to energy consumption [31, 32]. EF is the level of energy efficiency in a region.
It is necessary to estimate energy efficiency, particularly because it is not directly observable in an economic system. Therefore, this study estimates energy efficiency using a stochastic frontier energy demand function. Stochastic frontier functions generally measure the economic performance of production and operation processes and have therefore been applied to production or cost theory using an econometric approach. This approach is based on the notion that frontier functions produce the maximum output or minimum cost levels achievable by a producer. In a production function, the frontier represents the maximum production level for a given input. In a cost function, the frontier is the minimum cost for a given output. An energy demand function can thus be considered similar to a cost function. In other words, the difference between observed energy demand and minimized demand is the technical inefficiency observed when the output for a production activity is given. In an aggregate energy demand function, the frontier denotes the minimum energy level needed for the production activities in a region to achieve a given production level. In other words, by estimating an energy demand frontier function, it is possible to determine the baseline energy demand that reflects the energy demand in a region that is efficiently managing energy use through its production and operational processes. Additionally, it allows us to ascertain whether a region is on the frontier. If a region is not on the frontier, the distance from the frontier indicates the rate of energy consumption exceeding baseline demand (i.e., energy inefficiency) [33].
The panel SFA in this study follows the premise of Aigner et al. [34]. Further, this study adopts the one-step approach of Battese and Coelli [35]. It thus estimates the energy frontier function and the determinants of the energy inefficiency term simultaneously. Traditionally, a two-step estimation method is adopted, in which inefficiency is obtained by estimating the stochastic frontier function, and the value is regressed by determinants. In this case, a contradiction arises between the assumption of the distribution on the inefficiency term of the frontier function and the regression analyzing the inefficiency determinant. As such, the consistency of the estimation result is not guaranteed [36]. By adopting the one-step approach, we can avoid this problem. An SFA model using this approach approximates an economy’s energy efficiency level based on a one-sided non-negative error term. That is, this study assumes the log-log function type in Eq. (1) can be specified as follows:
where α is an estimated parameter. The error term
Improvements in energy efficiency can be achieved through social innovation in the production and consumption processes of energy services, as well as the technical and organizational factors of energy demand. Average energy efficiency in this study is formulated as:
where β is an estimated parameter, KL is the capital-labor ratio, and ER is the electrification rate for the industrial sector. If the factor of the inefficiency term improves the efficiency, the sign of β is negative.
Factories and offices with large-scale facilities have high energy consumption and low energy efficiency in production. For example, a petrochemical complex, the paper pulp manufacturing industry, and the steel industry have large-scale production facilities. Therefore, the energy efficiency levels of these industries are low. Meanwhile, labor-intensive factories and offices have compact-scale production facilities, thus low energy consumption and high energy efficiency. For example, labor-intensive process-assembled industries are more energy efficient than material-based industries [38]. To control the differences in local production industries, this study considers capital-labor ratio. The coefficient values for KL are expected to be positive.
Regions that use coal and kerosene tend to report higher carbon dioxide emissions than those using electricity. Further, areas with a low electrification rate are considered wasteful in terms of energy use. Electrification of factories and offices enables an efficient use of energy. For example, a factory energy management system (FEMS) can be introduced to electrify a factory. A FEMS functions in coordination with power generation, power storage, and energy saving devices, allowing for energy saving that industries have been unable to hitherto realize. Furthermore, the implementation of a building energy management system (BEMS) for commercial buildings could reduce energy consumption and control energy-related facilities. Consequently, energy efficiency could increase with a rise in electricity usage through promoting electrification. Therefore, the coefficient values for ER are expected to be negative.
Electrification can significantly influence the improvement of energy efficiency in a region. Therefore, this study conducts a quantitative analysis as an additional regression that account for the characteristics of factories and offices that may be electrification determinants.
The variables in the following equation are assumed to be determinants of a region’s electrification rate:
where j is a region (j = 1, …, J), t is the time (t = 1, …, T), ER is the electrification rate in the industrial sector, and LN is the number of employees per establishment, comprising offices and factories, and denotes the scale of an establishment. OR is the ratio of the number of offices to that of establishments; TFP is the total factor productivity and represents an establishment’s productivity level; CDD and HDD are cooling and heating degree days, respectively; and δ is an estimated parameter. Since this study uses panel data, δj denotes the fixed effect. In estimation of (4), it is necessary to consider endogeneity between the productivity and the electrification rate. It would be possible that a higher electrification rate also influences productivity. Although these endogeneity effects can be treated with a fixed effect model, it is not sufficient. To obtain robust results, this study calculates the estimates by panel GMM using instrumental variables in addition to the fixed effect model.
The data used for the analysis are 1990–2010 panel data for 47 prefectures. Data on the final energy consumption (E) of the sectors of each prefecture are taken from the Energy Consumption Statistics by Prefecture (Ministry of Economy, Trade, and Industry). The energy price index (P) is estimated using the real energy price index for the respective sector by the International Energy Agency (IEA). Income (Y) is a real gross regional expenditure, data for which are available in the Annual Report on Prefectural Accounts (Cabinet Office). The capital-labor ratio (KL) is the ratio of capital stock to the number of employees, and data for the number of employed persons are available in the Annual Report on Prefectural Accounts (Cabinet Office). Capital vintage (IK) is the ratio of capital investment to capital stock. Data on capital investment and stock are based on the data published by the Central Research Institute of Electric Power Industry. Data on CDD and HDD are from the prefectural government’s location and weather station—cooling degree day is the sum of the difference between average temperature on the days exceeding 24 and 22°C, while heating degree day is the sum of the difference between average temperatures below 14°C and above 14°C. The ER is estimated from the data in the Energy Consumption Statistics by Prefecture (Ministry of Economy, Trade, and Industry). The estimation for the percentage of offices for all establishments (OR) is based on the number of business establishments listed by the Economic Census (Ministry of Economy, Trade, and Industry). Data for productivity (TFP) are the total factor productivity calculated by Otsuka and Goto [39]. Table 1 presents the descriptive statistics.
Description | Variable | Mean | Std. dev. | Maximum | Minimum |
---|---|---|---|---|---|
Final energy consumption (TJ) | E | 199,829 | 214,836 | 1,181,999 | 24,530 |
Energy price index (2010 = 100) | P | 86.5 | 8.9 | 111.5 | 77.7 |
Income (JPY, millions) | Y | 10,422,755 | 14,063,661 | 100,931,767 | 1,865,830 |
Capital-labor ratio | KL | 15.48 | 3.51 | 26.19 | 7.27 |
Vintage | IK | 0.061 | 0.016 | 0.118 | 0.035 |
Cooling degree day | CDD | 367.0 | 175.6 | 1186.1 | 0.0 |
Heating degree day | HDD | 1106.3 | 470.9 | 2769.2 | 0.2 |
Electrification rate (%) | ER | 36.18 | 12.08 | 59.29 | 8.55 |
Establishment size (person) | LN | 9.34 | 0.87 | 12.12 | 7.08 |
Office ratio (%) | OR | 94.92 | 1.62 | 98.20 | 89.67 |
TFP index | TFP | 0.185 | 0.112 | 0.662 | −0.055 |
Descriptive statistics.
Sources: For final energy consumption, see Energy Consumption Statistics by Prefecture (Ministry of Economy, Trade and Industry:
Table 2 presents the regional characteristics for Japan as of 2010. Particularly, large metropolitan areas, such as the Greater Tokyo Area, Kansai, and Chubu, report high energy consumption. Moreover, the income scale is large and vintage is high in these areas. The capital-labor ratio is relatively high because the manufacturing industry is concentrated in the Chubu and Hokuriku regions. The degree of air conditioning usage is significant in the warm western Japan, and the number of heating days is high in eastern Japan. Further, the Greater Tokyo Area, Kansai, and Chubu have several large-scale business establishments and productivity tends to be high here.
Panel A | |||||||
---|---|---|---|---|---|---|---|
Final energy consumption (TJ) | Energy price index (2010 = 100) | Income (JPY, millions) | Capital-labor ratio | Vintage | Cooling degree day | Heating degree day | |
Region | E | P | Y | KL | IK | CDD | HDD |
Hokkaido | 322,771 | 100.00 | 19,199,451 | 15.09 | 0.035 | 124.0 | 2591.2 |
Tohoku | 84,446 | 100.00 | 5,948,899 | 18.69 | 0.042 | 315.4 | 1907.7 |
Kita-Kanto | 170,132 | 100.00 | 7,908,596 | 19.67 | 0.046 | 450.4 | 1407.0 |
Greater Tokyo area | 613,806 | 100.00 | 41,983,820 | 19.21 | 0.046 | 492.5 | 1060.9 |
Chubu | 234,453 | 100.00 | 14,956,162 | 21.40 | 0.046 | 511.0 | 1270.3 |
Hokuriku | 55,461 | 100.00 | 4,209,377 | 20.81 | 0.043 | 476.2 | 1522.8 |
Kansai | 211,767 | 100.00 | 13,566,039 | 20.86 | 0.047 | 556.5 | 1116.0 |
Chugoku | 269,740 | 100.00 | 5,900,042 | 20.52 | 0.045 | 539.2 | 1194.3 |
Shikoku | 75,918 | 100.00 | 3,549,752 | 19.05 | 0.046 | 572.4 | 910.6 |
Kyushu | 138,517 | 100.00 | 6,618,666 | 18.32 | 0.047 | 545.1 | 911.8 |
Okinawa | 38,462 | 100.00 | 3,850,416 | 12.97 | 0.052 | 909.0 | 122.2 |
Panel B | |||||||
Electrification rate (%) | Establishment size (person) | Office ratio (%) | TFP index | ||||
Region | ER | LN | OR | TFP | |||
Hokkaido | 34.69 | 9.84 | 97.67 | 0.25 | |||
Tohoku | 44.58 | 9.27 | 96.33 | 0.15 | |||
Kita-Kanto | 46.78 | 9.53 | 95.23 | 0.27 | |||
Greater Tokyo area | 30.67 | 11.08 | 96.82 | 0.40 | |||
Chubu | 44.25 | 9.70 | 94.77 | 0.26 | |||
Hokuriku | 51.66 | 8.84 | 95.00 | 0.22 | |||
Kansai | 40.63 | 9.34 | 95.82 | 0.32 | |||
Chugoku | 28.35 | 9.53 | 96.47 | 0.19 | |||
Shikoku | 38.06 | 8.55 | 96.59 | 0.21 | |||
Kyushu | 38.78 | 9.53 | 97.13 | 0.15 | |||
Okinawa | 49.24 | 8.24 | 98.20 | 0.14 |
Regional characteristics for Japan (as of 2010).
Notes: the regional classification is as follows: Hokkaido (Hokkaido), Tohoku (Aomori, Iwate, Miyagi, Akita, Yamagata, Fukushima, and Niigata), Tokyo (Saitama, Chiba, Tokyo, Kanagawa, Ibaraki, Tochigi, Gunma, and Yamanashi), Hokuriku (Toyama, Ishikawa, and Fukui), Chubu (Nagano, Gifu, Shizuoka, Aichi, and Mie), Kansai (Shiga, Kyoto, Osaka, Hyogo, Nara, and Wakayama), Chugoku (Tottori, Shimane, Okayama, Hiroshima, and Yamaguchi), Shikoku (Tokushima, Kagawa, Ehime, and Kochi), Kyushu (Fukuoka, Saga, Nagasaki, Kumamoto, Oita, Miyazaki, and Kagoshima), and Okinawa (Okinawa).
Table 3 presents the estimation results for the energy demand frontier function. Model A shows the estimation results of (2) and (3). Model B shows the estimation result of the model considering a nonlinear effect in the inefficiency determinant.
Model A | Model B | |||
---|---|---|---|---|
Coefficient | Standard error | Coefficient | Standard error | |
−0.584** | (0.038) | −0.571** | (0.040) | |
−0.046** | (0.009) | −0.039** | (0.009) | |
0.707** | (0.010) | 0.705** | (0.010) | |
0.10 ** | (0.023) | 0.109** | (0.021) | |
0.065** | (0.010) | 0.073** | (0.011) | |
−0.021* | (0.009) | −0.021* | (0.009) | |
−0.005 | (0.007) | −0.006 | (0.007) | |
0.534** | (0.043) | 0.556** | (0.047) | |
0.093** | (0.025) | 0.096** | (0.024) | |
−0.028** | (0.009) | |||
−0.522** | (0.011) | −0.556** | (0.026) | |
−0.019 | (0.011) | |||
0.062** | (0.004) | 0.063** | (0.004) | |
0.692** | (0.096) | 0.690** | (0.092) | |
Number of observations | 987 | 987 |
Estimation results for 1980–2010.
Note: ** and * denote significance at the 1 and 5% levels, respectively.
First, let us consider the results of Model A. The estimated coefficients show the expected signs, and all variables are statistically significant. Since each variable is a logarithmic variable, the estimated parameter can be interpreted as elasticity. Estimated price elasticity is 0.046 and income elasticity 0.707, indicating that income elasticity significantly exceeds price elasticity. Price elasticity is inelastic and denotes the nature of energy goods as essential goods. The capital-labor ratio and the coefficient on vintage are positive and have reasonable signs. This suggests that there is more energy demand in areas where industries for which mechanization is progressing are located. It also shows that capital investment increases energy demand. The coefficients for cooling degree day are slightly significant, but their magnitude is small. Additionally, the coefficients for heating degree day are not significant. These results show that temperature is a weak determinant of energy demand in the industrial sector.
Next, this study evaluates the estimation results for the factors determining energy efficiency. The capital-labor ratio is positive and reports the expected sign. This indicates that energy efficiency deteriorates with an increase in mechanization. In other words, energy efficiency is lower in regions where numerous industries with large-scale facilities are located. The electrification rate is negative, reporting the expected sign. The results show that an increase in the electrification rate enhances energy efficiency. The coefficient value for the electrification rate is considerably larger than that for the capital-labor ratio. That is, the positive impact of installing power facilities and an increase in the electrification rate from office automation are more significant than the negative impact of installing large productive capital equipment.
Due to these effects, this study estimates Model B, which account for the nonlinear effects of determinants. Statistically, significant values are obtained for the quadratic term of the capital-labor ratio. The sign of the quadratic term is negative. This shows there is a threshold for the impact of the capital-labor ratio on energy efficiency. Specifically, the increase in mechanization improves energy efficiency, but it exacerbates energy efficiency when it exceeds a threshold value. On the other hand, the quadratic terms for electrification are not statistically significant, and the nonlinear effects of electrification are not recognizable.
During the observation period (1990–2010), there was financial crisis in 2008. As the economic depression spreads worldwide from the US, Japan’s economic growth rate fell greatly in 2008. This economic downturn had a significant influence on the production system of the regional industry. Hence, when considering result stability, this influence must be considered. Therefore, the analysis period is reset from 1990 to 2007, and Model A and Model B reestimated. Table 4 shows the reestimation results. Model C represents the result of reestimation of Model A and Model D of Model B. There is no significant difference in the regression coefficients in Table 4. Therefore, the results in Table 3 can be judged as robust.
Model C | Model D | |||
---|---|---|---|---|
Coefficient | Standard error | Coefficient | Standard error | |
−0.577** | (0.044) | −0.576** | (0.049) | |
−0.063** | (0.014) | −0.056** | (0.014) | |
0.709** | (0.011) | 0.706 ** | (0.011) | |
0.121** | (0.027) | 0.118** | (0.027) | |
0.073** | (0.012) | 0.079** | (0.012) | |
−0.018 | (0.009) | −0.018 | (0.009) | |
−0.006 | (0.008) | −0.008 | (0.007) | |
0.520** | (0.051) | 0.552** | (0.058) | |
0.094** | (0.029) | 0.091** | (0.029) | |
−0.028* | (0.010) | |||
−0.522** | (0.012) | −0.543** | (0.028) | |
−0.012 | (0.012) | |||
0.062 ** | (0.004) | 0.063** | (0.004) | |
0.648** | (0.111) | 0.667** | (0.106) | |
Number of observations | 846 | 846 |
Estimation results for 1980–2007.
Note: ** and * denote significance at the 1 and 5% levels, respectively.
Table 5 presents the descriptive statistics for the energy efficiency values of each prefecture, obtained from the estimation results (Model A). An efficiency value of 1 denotes the highest efficiency, while a value below 1 indicates lower energy efficiency. The average energy efficiency value is 0.617 and the median value 0.685. More importantly, a maximum value of 0.950 and a minimum of 0.114 point to a significant difference in energy efficiency levels among regions. Figure 1 shows the time-series transition of the national average energy efficiency scores. The average energy efficiency score of Japan’s industrial sector has been consistently increasing until 2007, after declining from 1990 to 1994. Energy conservation progressed from the latter half of the 1990s to the 2000s, and energy efficiency thus improved. However, in 2008, energy efficiency worsened and then increased slightly.
Mean | 0.617 |
Std. dev. | 0.234 |
Minimum | 0.114 |
Maximum | 0.950 |
Median | 0.685 |
Descriptive statistics of energy efficiency scores.
Note: The energy efficiency scores in the table are calculated using the estimation results for Model A.
Time trend of national average energy efficiency scores.
Table 6 presents the average energy efficiency scores for each prefecture and ranks them accordingly. Nara, Tokyo, Yamanashi, Ishikawa, Saga, and Yamagata are among the high-ranking areas. The factories and offices located in these areas are likely to report an increasing rate of electrification. On the other hand, Oita has the lowest energy efficiency. Okayama, Chiba, and Yamaguchi have petrochemical complexes and, thus, low energy efficiency, because petrochemical complexes have large-scale production facilities and are lagging in electrification, given the large demands for coal, kerosene, and gas for production.
Rank | Prefecture | Average efficiency score | Change rate of score |
---|---|---|---|
1 | Nara | 0.93 | −0.22 |
2 | Tokyo | 0.92 | −0.10 |
3 | Yamanashi | 0.91 | −0.10 |
4 | Ishikawa | 0.90 | −0.20 |
5 | Saga | 0.87 | −0.06 |
6 | Yamagata | 0.86 | −0.01 |
7 | Nagano | 0.86 | 0.17 |
8 | Kyoto | 0.85 | −0.19 |
9 | Okinawa | 0.84 | −0.07 |
10 | Gunma | 0.84 | 0.00 |
11 | Akita | 0.84 | −0.46 |
12 | Fukui | 0.83 | −0.01 |
13 | Kumamoto | 0.79 | −0.38 |
14 | Fukushima | 0.79 | 0.73 |
15 | Shiga | 0.77 | 0.81 |
16 | Shimane | 0.77 | 0.61 |
17 | Tochigi | 0.77 | −0.18 |
18 | Nagasaki | 0.76 | 0.79 |
19 | Gifu | 0.72 | −0.13 |
20 | Saitama | 0.72 | −0.14 |
21 | Kagoshima | 0.72 | −0.29 |
22 | Tottori | 0.71 | −1.07 |
23 | Miyagi | 0.71 | −0.59 |
24 | Iwate | 0.68 | 0.15 |
25 | Toyama | 0.67 | 0.51 |
26 | Shizuoka | 0.65 | 0.81 |
27 | Tokushima | 0.64 | 1.39 |
28 | Miyazaki | 0.63 | 0.82 |
29 | Osaka | 0.61 | 0.16 |
30 | Niigata | 0.60 | 0.11 |
31 | Aichi | 0.53 | 0.23 |
32 | Aomori | 0.53 | 0.38 |
33 | Hokkaido | 0.51 | 0.10 |
34 | Kochi | 0.49 | 0.43 |
35 | Kagawa | 0.47 | 0.77 |
36 | Hyogo | 0.42 | 0.35 |
37 | Fukuoka | 0.42 | 1.12 |
38 | Ehime | 0.40 | 0.15 |
39 | Kanagawa | 0.37 | −0.58 |
40 | Wakayama | 0.31 | 1.36 |
41 | Hiroshima | 0.29 | 0.53 |
42 | Mie | 0.28 | 1.83 |
43 | Ibaraki | 0.25 | −0.26 |
44 | Yamaguchi | 0.20 | −0.25 |
45 | Chiba | 0.15 | −0.32 |
46 | Okayama | 0.14 | 0.49 |
47 | Oita | 0.13 | 0.95 |
Average energy efficiency scores and change rate of the score.
Note: The energy efficiency scores in the table are calculated using the estimation results for Model A.
Table 6 also shows the change rate of the average scores for the energy efficiency between the 1990s and the 2000s. The table highlights two key characteristics. First, Mie, Wakayama, and Fukuoka report improved average scores. Specifically, Mie has the highest improvement score, and its energy efficiency value shows an annual improvement of 1.87%. Wakayama and Fukuoka’s scores improve by 1.36 and 1.12% annual rates. These prefectures rank low in average energy efficiency. Therefore, it is highly likely these regions have several electrical machineries and equipment manufacturing units, and their machinery industry has progressive electrification rates, thus contributing to the improvement of energy efficiency. Second, energy efficiency is deteriorating in regions with high energy efficiency levels, including Nara, Tokyo, Yamanashi, Ishikawa, and Saga. This suggests that the regional disparities in energy efficiency are decreasing.
This study verifies the possibility of reducing regional disparities in energy efficiency by calculating the rank correlation coefficient between the average energy efficiency score and its change rate. Table 7 shows the results of the rank correlation coefficient. The Kendall rank correlation coefficient is −0.2396, which is statistically significant. Spearman’s rank correlation coefficient is −0.3207, also being statistically significant. The sign of any rank correlation coefficient is negative, and the improvement in energy efficiency is progressing in the region with a low energy efficiency.
Rank correlation method | Rank correlation coefficient | P-value |
---|---|---|
Kendall’s tau | −0.2396 | 0.0175 |
Spearman | −0.3207 | 0.0280 |
Results of rank correlation.
The energy efficiency level is highly related to electrification. Figure 2 is a cross-sectional plot of the average values for the electrification rate and energy efficiency. The figure clearly illustrates an upward trend. In other words, regions with advanced electrification have high energy efficiency levels. Specifically, regions where offices are concentrated (e.g., Tokyo) are located in the upper right corner, while those with petrochemical complexes (e.g., Oita, Okayama, and Chiba) are in the lower right.
Static relationship between energy efficiency score and electrification rate.
Furthermore, it is also highly possible that energy efficiency improvements have progressed to electrification. Figure 3 plots the time-series relationship between the electrification rate and energy efficiency and shows an upward trend. In other words, it is highly likely that advanced electrification contributes to energy efficiency improvements. As described above, Mie is likely to report improved energy efficiency, given its progress in electrification. On the other hand, Chiba has lower energy efficiency, given the low energy efficiency of petrochemical complexes.
Dynamic relationship between energy efficiency score and electrification rate.
Finally, this study analyzes the determinants of the electrification rate, which is one of the key factors to improve energy efficiency. Table 8 presents the estimation results for Eq. (4). First, the F-test checks for fixed effects and rejects the null hypothesis that there is no fixed effect at the 1% significance level. Additionally, the Hausman test rejects the null hypothesis that the fixed effect is a random effect at the 1% significance level. Therefore, the fixed effect model is appropriate for the panel regression analysis. Further, to test the validity of the panel GMM estimation, this study performs a Sargan-Hansen test for the exogeneity of the instrumental variables. From Hansen J’s statistical results, the number of instrumental variables is appropriate and satisfies the condition of heteroskedasticity.
Method | EF | Panel GMM | ||
---|---|---|---|---|
Coefficient | Standard error | Coefficient | Standard error | |
−0.434** | (0.196) | 0.240 | (0.264) | |
0.215** | (0.080) | 0.331** | (0.084) | |
3.751** | (0.328) | 4.781** | (0.368) | |
0.707** | (0.008) | 0.550** | (0.037) | |
0.001 | (0.003) | 0.000 | (0.003) | |
0.000 | (0.008) | 0.001 | (0.008) | |
Number of observations | 987 | 940 | ||
Adjusted R-squared | 0.9850 | 0.9840 | ||
Hausman test | 17.086** | |||
Prob (Hausman) | [0.0043] | |||
J-statistic | 0.0122 | |||
Prob(J-statistic) | [0.9119] | |||
Instrument | TFP(−1) |
Panel estimation results on determinants of the electrification rate.
Note: 1. ** and * indicate significance at the 1 and 5% levels, respectively. 2. The values between parentheses are p-values.
The signs for all the variables are consistent under both models. The sign for the establishment size is positive, meaning establishments with a larger number of employees have a higher electrification rate. Further, the higher the proportion of offices, the greater the electrification rate. It is also noteworthy that the sign of an establishment’s productivity is positive. This indicates that an increase in the establishment’s productivity is proportional to that in the electrification rate. The magnitude of the coefficient on productivity is between 0.475 and 0.676, and it significantly influences the electrification rate. Neither cooling nor heating degree days are statistically significant.
In sum, the establishment scale and productivity are closely related to the electrification rate, which may influence energy efficiency. That is, productivity improves energy efficiency through an increase in electrification at factories and business establishments. Therefore, the efforts to increase the office productivity could improve energy efficiency.
This study analyzed the energy efficiency levels and their determinants in Japan’s industrial sector using an energy demand frontier function. To the best of the author’s knowledge, this is the first attempt to do so. Energy intensity has been traditionally used as a proxy for energy efficiency and depends on economic variables such as price and income. However, this study specified energy demand and controlled for price, income, production environment, and climate factors, thus rendering energy efficiency a more accurate index.
This study focused on compact mechanization and electrification as the two main determinants of the improvements in the energy efficiency of the industrial sector. The analysis presented three key findings. First, an installment in large capital facilities deteriorates energy efficiency. Therefore, policies aimed at promoting small- or medium-sized production facility installments lead to improvements in energy efficiency. Second, an increase in the electrification rate of a given region can improve its energy efficiency. Finally, it is necessary to increase the productivity and also the electrification rate, that is, raising the productivity of factories and offices promotes electrification, which considerably contributes to increased energy efficiency. This finding highlights the relationship between increasing productivity and improvements in energy efficiency, suggesting the possibility of the Porter hypothesis being established.
It can be concluded that the energy efficiency of the industrial sector can be improved by developing an appropriately competitive environment and encouraging electrification in each region’s energy market. Additionally, electrification increases environmental efficiency by reducing carbon dioxide emissions. Therefore, the promotion of electrification is critical to the achievement of not only energy efficiency but also improving environmental efficiency. Nevertheless, further research is needed to verify whether this trend also applies to other countries to ensure the effectiveness of electrification.
The future research agenda relates to both the micro and macro viewpoints. The former indicates that future studies should examine the energy efficiency of electric power as an energy source from a more diversified viewpoint, including power saving. An important factor that warrants consideration in power consumption efficiency is an appropriate way to account for the efficiency of plant facilities and performance of air conditioning. Since this research could not account for the performance of each device, quantitatively examining this factor warrants further research.
The research agenda from the macro viewpoint clarifies how the increase in urban population density affects energy efficiency, as discussed in Otsuka and Goto [40] and Otsuka [41]. In developed countries, urban compactification is being promoted from the viewpoint of city sustainability. The rise in urban population density exacerbates energy efficiency by causing a heat island phenomenon. Meanwhile, population concentration in cities has the merit of promoting the use of public transportation. Further, cities have more dwelling units than detached houses, and apartments have high thermal insulation and energy efficiency. As such, living in the city center may increase the energy efficiency. It seems that clarifying these problems would deepen the understanding of energy efficiency.
This study was funded by the Japan Society for the Promotion of Science (grant no. 18K01614).
The author declares no conflict of interest. The funders had no role in the design of the study; the collection, analyses, or interpretation of data; the writing of the manuscript, or the decision to publish the results.
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