{"id":20409,"date":"2023-06-09T12:05:05","date_gmt":"2023-06-09T11:05:05","guid":{"rendered":"https:\/\/www.vtei.cz\/?p=20409"},"modified":"2024-08-19T18:52:11","modified_gmt":"2024-08-19T17:52:11","slug":"factors-affecting-the-cost-of-drinking-water-production","status":"publish","type":"post","link":"https:\/\/www.vtei.cz\/en\/2023\/06\/factors-affecting-the-cost-of-drinking-water-production\/","title":{"rendered":"Factors affecting the cost of drinking water production"},"content":{"rendered":"

ABSTRACT<\/h2>\n

The article summarizes the findings of a statistical analysis of the cost of drinking water production in the Czech Republic in 2018. Understanding the factors that influence the cost of drinking water production is important for choosing a cost-effective public drinking water supply system. We present the first study analysing the factors affecting the cost of drinking water production in\u00a0the Czech Republic. We tested the following factors for their influence on the\u00a0production costs of drinking water: the quantity of drinking water produced, the type of\u00a0raw water (surface vs. groundwater), electricity consumption, and the treatment technologies and chemicals applied. The results suggested that drinking water production from groundwater was cheaper than from surface water. At the same time, some water treatment technologies and usage of some treatment technologies and chemicals increase production costs. The\u00a0use of sodium hypochlorite, chlorine and demanganisation have the\u00a0greatest impact on production costs. We\u00a0have also confirmed economies of scale in the production of drinking water.<\/span><\/p>\n

INTRODUCTION<\/h2>\n

The cost of drinking water treatment depends on the quality of raw water, treatment technologies, legal regulations, used energy sources, and the amount of treated water [1]. Regarding technological processes for water treatment, the\u00a0use of gravity filtration and chlorine application has the greatest impact on costs. The cost of drinking water production is also influenced by the distance over which the water is distributed from the producer to the customer, and the method of this transport [1].<\/p>\n

One of the most important factors affecting the cost of drinking water production is the quality of raw water. Numerous studies have found that improving source water quality reduces its treatment costs [2]. Due to\u00a0 greater natural purification, groundwater is usually considered cleaner than surface water [3], and the cost of treating it is lower than that of surface water [4].<\/p>\n

Natural water purification is one of the most frequently mentioned benefits that nature provides to people, so-called ecosystem services [5]. Although the demand for valuing water-related ecosystem services is growing [6], research in this area is still scarce [7]. Valuation of the ecosystem service of groundwater purification has so far only been carried out in the Netherlands, using the replacement cost method [4]. Using this method, the value of groundwater purification can be calculated as the difference between surface and groundwater treatment costs. To use this method, it is therefore necessary to know how the costs of producing drinking water from surface water and groundwater sources differ. However, this issue has not yet been investigated in the\u00a0Czech Republic.<\/p>\n

Previous research into factors affecting drinking water production costs has focused primarily on North America and Western Europe. Therefore, we focused on Central Europe and analysed the costs of drinking water production in\u00a0the\u00a0Czech Republic. According to our information, this is the first study to examine parameters affecting the costs of drinking water production in Central Europe.<\/p>\n

DATA<\/h2>\n

The data was obtained by combining the data that owners and operators of\u00a0water supply and sewerage systems must report annually to the relevant water authorities (selected data from property records and selected data from operational records of water supply and sewerage systems, so-called V\u00daME and V\u00daPE data). This data was supplemented with other data, e.g. rates of charges for\u00a0water abstraction. We performed the analysis on data for 2018.<\/p>\n

We excluded observations from the database which had too low water production as well as those with too low or too high unit production costs. We assumed that these observations were entered incorrectly. We also excluded three abstraction points where more than 50 % of water production was technological water. In addition, we also excluded locations where infiltration is\u00a0used. After cleaning the data, 3,253 observations remained (the total number of observations before cleaning was 3,566).<\/p>\n

METHODOLOGY<\/h2>\n

In the short term, the costs of companies using environmental inputs are determined by the volume of production, company characteristics, costs of non-environmental inputs, costs of fixed factors, and characteristics of the natural capital used (non-environmental input) [5, 8].<\/span><\/p>\n

Since we were not interested in the effects on total costs but on unit production costs, we used the following function based on previous research:<\/span><\/p>\n\"\"<\/a>\n

where<\/p>\n

JNBP\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 are unit production costs without water abstraction charges<\/p>\n

VV\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0is amount of water produced unit consumption of electrical energy (kWh\/m3<\/sup> of water produced)<\/p>\n

PDV_d\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0binary variable characterizing the type of\u00a0raw\u00a0water<\/p>\n

prom\u011bnn\u00e9 TECH 1\u201330\u00a0 \u00a0 are binary variables characterizing the technologies and chemicals used in water treatment<\/p>\n

\u03b1\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0is constant<\/p>\n

\u03b21\u2013\u03b23, b1\u2013b30\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 are regression coefficients<\/p>\n

e\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 is an error term<\/p>\n

Tab. 1. Descriptive statistics<\/h5>\n\"\"<\/a>\n

Since there is often a non-linear relationship between costs and the amount of produced water [1], we used natural logarithm of production volume (ln VV). The PDV_d variable was equal to 1 if the proportion of groundwater in the total water production at a given abstraction point was equal to or greater than 50\u00a0%. We had information on 17 technologies and 13 chemicals used in water treatment. However, some of these technologies and chemicals are not used very often, or their use is not frequent according to the V\u00daME database for the analysed year (2018). For the statistical analysis, we used only the following 13\u00a0technologies and chemicals with 5 % or more use in the year under review:<\/p>\n