Method for calculating water unhooking index driving effect for regional industry

Abstract

The invention relates to a calculation method of a regional industrial water unhooking index driving effect, and belongs to the technical field of water resource utilization and management. Aiming at the problems of weak universality, high technical threshold and the like of the existing unhooking relationship analysis method, the method comprises the following steps of: constructing an unhooking elastic coefficient model of industrial water taking quantity and industrial economic growth based on a Tapio unhooking model, determining influence factors of industrial water taking quantity change by utilizing a kaya identical equation, and decomposing the influence factors by adopting a factor decomposition method to obtain an unhooking relationship analysis result; a driving effect decomposition model of the industrial water taking amount is obtained, the driving effect decomposition model is combined with an unhooking elastic coefficient model, an unhooking index decomposition model of the industrial water taking amount and industrial economic growth is obtained, and the unhooking index decomposition model can calculate analysis objects of different scales. And obtaining a corresponding unhooking index driving effect and a contribution degree interannual change result thereof. The method is simple, efficient and high in applicability, can meet actual management requirements, and has important value and application potential.

Description

Technical Field

[0001] The present invention relates to the technical field of water resource utilization and management, and in particular to a method for calculating the driving effect of a regional industrial water use decoupling index. Background Art

[0002] Generally speaking, the growth rate of energy consumption is often used as an important indicator to judge the economic situation. However, in recent years, with the advancement of science and technology and the improvement of energy efficiency, the industrial structure and energy structure have been constantly changing, and there is no obvious correlation between economic growth and energy consumption, resulting in the gradual "decoupling" of economic growth and energy consumption. Therefore, the growth rate of energy consumption can no longer be simply used as a reference for economic growth.

[0003] "Decoupling" means breaking the correlation between resources and environment and economic growth, so that resource consumption is no longer restricted. "Decoupling" can be divided into strong decoupling and weak decoupling. Strong decoupling means that the growth rate of energy consumption is lower than that of economic growth, while the energy consumption volume has decreased. Weak decoupling means that the growth of energy consumption slows down, but the total energy consumption is still increasing. In addition, there are also recessionary decoupling and other situations.

[0004] At present, the decoupling relationship analysis is mainly based on virtual water trade, water footprint combined with the extended LMDI model for research. Some scholars also use the VAR model, pulse response function and variance decomposition method, or Kuznets curve and other technical methods to conduct industrial water decoupling analysis; however, these models are generally more theoretical and less universal, and such models require a large amount of relevant data, and the research results are difficult to promote and apply. In addition, such models have high technical barriers and have high requirements on the professional and technical level of engineering and management personnel, which makes such models have great limitations in promotion and decoupling analysis.

[0005] Clarifying the dynamic decoupling relationship between industrial economic growth and industrial water consumption and its driving factors has important theoretical and practical significance for industrial water conservation, improving the level of industrial refined management, and achieving sustainable utilization of regional industrial water resources. Therefore, studying a simple, efficient and applicable calculation method for the decoupling driving effect has great application potential in the industry. Summary of the invention

[0006] In view of the deficiencies in the above-mentioned prior art, the present invention aims to provide a method for calculating the driving effect of the regional industrial water decoupling index. Based on the Kaya identity expansion and the Dirichlet mean index decomposition (LMDI) method, it is applied to the quantitative calculation of the industrial water decoupling index driving effect and its contribution of regional industrial clusters in different years during the analysis period. The results show that this method is concise, efficient, and highly applicable, and can better meet actual management needs, thereby solving the problems existing in the above-mentioned background technology.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0008] On the one hand, the present invention discloses a method for calculating the driving effect of a regional industrial water decoupling index, comprising the following steps:

[0009] S1: Based on the Tapio decoupling model, a decoupling elasticity coefficient model between industrial water withdrawal and industrial economic growth is constructed;

[0010] S2: Use the Kaya identity to determine the factors affecting changes in industrial water withdrawal;

[0011] S3: The factor decomposition method is used to decompose the influencing factors of industrial water withdrawal changes, and the driving effect decomposition model of industrial water withdrawal is obtained;

[0012] S4: Combine the driving effect decomposition model with the decoupling elasticity coefficient model to establish a decoupling index decomposition model of industrial water withdrawal and industrial economic growth, and calculate the decoupling index of the driving effect based on the decoupling index decomposition model.

[0013] As a further preferred solution of the above technical solution: in step S1, the expression of the decoupling elastic coefficient model is:

[0014]

[0015] Where: R V,G is the decoupling elasticity coefficient; ΔV is the change in industrial water withdrawal; V is the industrial water withdrawal; ΔG is the change in GDP, and G is the GDP.

[0016] As a further preferred solution of the above technical solution: in step S2, the Kaya identity is used for expansion to obtain the influencing factors of the change in industrial water withdrawal, wherein the expression of the industrial water withdrawal in the tth year is:

[0017]

[0018] Where: V t is the industrial water withdrawal in year t; Water intake of the i-th industrial sector in year t; G t is the industrial added value in year t; Industrial added value of the i-th industrial sector in year t; is the industrial water reuse rate of the i-th industrial sector in year t; is the water consumption of the i-th industrial sector in year t; is the ratio of industrial non-reusable water consumption occupied by industrial sector u in year t; is the proportion of the added value of the i-th industrial sector in the t-th year to the added value of the industry in that year; It is the water consumption per 10,000 yuan of added value of the i-th industrial sector in the t-th year.

[0019] A further preferred embodiment of the above scheme is as follows: In step S3, the LMDI method is used to decompose the influencing factors of the change in industrial water intake, and the industrial water intake is increased from the base period (V 0 ) to the calculation period (V t ) change (ΔV) is expressed as follows:

[0020] ΔV=V t -V 0 =ΔV P +ΔV G +ΔV S +ΔV I (3)

[0021] Where: ΔV G The industrial scale driving effect of economic output; ΔV p represent The driving effect of It is only closely related to the reuse rate of industrial water, so ΔV is defined p To save water and improve the effect; ΔV s represent The driving effect of ΔV s It is the effect of industrial structure; ΔV i represent The driving effect of ΔV i For water efficiency.

[0022] As a further preferred solution: Based on the LMDI method, the driving effect decomposition model of industrial water withdrawal is obtained as follows:

[0023]

[0024]

[0025] A further preferred embodiment of the above scheme is as follows: in step S4, the expression of the decoupling index decomposition model of industrial water withdrawal and industrial economic growth is as follows:

[0026]

[0027] Where: D P , D G , D S , D I They are water conservation progress effect decoupling index, industrial scale effect decoupling index, industrial structure effect decoupling index and water use efficiency effect decoupling index.

[0028] On the other hand, the present invention further discloses an electronic device, including a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory to implement the steps of any of the above methods.

[0029] In yet another aspect, the present invention further discloses a computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the steps of any of the above methods.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. Based on the Kaya identity expansion and the Dirichlet mean index decomposition (LMDI) method, the present invention proposes a quantitative calculation method for the contribution of industrial clusters to the changes in the regional industrial water decoupling index and its driving effect. The method has strong versatility and can be applied not only to industrial clusters, but also to industrial agglomeration areas, industrial parks, etc. It corresponds to the quantitative calculation of the contribution of industrial water decoupling index changes and the driving effect in various scales such as provinces, cities or counties, and has important practical value and application potential.

[0032] 2. The method of the present invention is simple and efficient, with a low technical threshold, and can better meet actual management needs. It has important guiding significance for achieving industrial refined management and sustainable utilization of industrial water resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solution in the embodiment of the present invention, the traditional pillar industry cluster of Henan Province is taken as an example to briefly introduce the drawings required in the example.

[0034] Figure 1 It is a graph showing the driving effect of the industrial water decoupling index of the traditional pillar industry cluster in the present invention;

[0035] Figure 2 It is a graph showing the contribution of the driving effect of the industrial water decoupling index of the traditional pillar industry clusters in the present invention;

[0036] Figure 3 It is a graph showing the total driving effect of the industrial water decoupling index of different industries in the traditional pillar industry clusters of the present invention;

[0037] Figure 4 It is a graph showing the contribution of the total driving effect of the industrial water decoupling index of different industries in the traditional pillar industry clusters of the present invention;

[0038] Figure 5 It is a graph showing the driving effect of the decoupling index of industrial water use in the thermal power industry in the traditional pillar industry cluster of the present invention;

[0039] Figure 6This is a graph showing the contribution changes of the driving effect of the decoupling index of industrial water use in the thermal power industry in the traditional pillar industry clusters of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme in the embodiments will be described clearly and completely in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments, not all of the embodiments. For ordinary technicians in this field, other embodiments can be obtained without creative work.

[0041] Reference Figures 1 to 6 The present invention proposes a method for calculating the driving effect of the regional industrial water decoupling index, comprising the following steps:

[0042] (1) Based on the Tapio decoupling model, a decoupling elasticity coefficient model between industrial water withdrawal and gross national product (GDP) is constructed, and its expression is as follows:

[0043]

[0044] Where: R V,G is the decoupling elasticity coefficient; ΔV is the change in industrial water withdrawal; V is the industrial water withdrawal; ΔG is the change in GDP, and G is the GDP.

[0045] (2) Use the Kaya identity to determine the factors affecting changes in industrial water withdrawal;

[0046] In 1990, Japanese scholar Yoichi Kaya proposed the Kaya identity, which established the relationship between CO2 emissions and energy consumption, economic growth and population. Due to its simple structure and easy solution, it has been widely used in the fields of environment and energy. Therefore, the present invention adopts the Kaya identity method for expansion and applies it to the research on industrial water use and economic growth in the present invention, so as to determine the influencing factors of industrial water consumption changes. After expanding the Kaya identity, the expression of industrial water withdrawal in the tth year is obtained as follows:

[0047]

[0048] Where: V t is the industrial water withdrawal in year t; Water intake of the i-th industrial sector in year t; G t is the industrial added value (GDP) in year t; Industrial added value of the i-th industrial sector in year t; is the industrial water reuse rate of the i-th industrial sector in year t; is the water consumption of the i-th industrial sector in year t; is the ratio of industrial non-reusable water consumption to water consumption of the i-th industrial sector in year t; is the proportion of the added value of the i-th industrial sector in the t-th year to the added value of the industry in that year; It is the water consumption per 10,000 yuan of added value of the i-th industrial sector in the t-th year.

[0049] (3) The factor decomposition method was used to decompose the influencing factors of industrial water withdrawal changes and obtain the driving effect decomposition model of industrial water withdrawal;

[0050] Based on the clarification of the influencing factors of changes in regional industrial water withdrawal, the factor analysis method is used to decompose the changes in industrial water withdrawal and determine the driving effect and driving effect value.

[0051] Decomposition methods include index decomposition method IDA (Index Decomposition Analysis) and structural decomposition method SDA (Structure Decomposition Analysis). SDA is based on input-output tables, while index decomposition method is based on decomposition. Compared with structural decomposition method, IDA has the advantage of smaller data requirements, can be applied to time series or cross-sectional data analysis, is mathematically rigorous and feasible, and is easy to operate. Commonly used index decomposition methods include Laspeyres index method and Dimitrov index method (LMDI). Among them, LMDI method has achieved many research results in the fields of energy economics and environmental protection at home and abroad due to its advantages such as complete decomposition, no residual terms, and better solution to zero value problems.

[0052] Therefore, the present invention uses the LMDI method to decompose the change of industrial water withdrawal. According to formula (2), the industrial water withdrawal in the base period (V 0 ) to the tth year of the calculation period (V t ) can be expressed as:

[0053] ΔV=V t -V 0 =ΔV P +ΔV G +ΔV S +ΔV I (3)

[0054] Where: ΔV G is the industrial scale driving effect of economic output, ΔV P represent The driving effect of It is only closely related to the reuse rate of industrial water, so ΔV is defined P It has an improved effect on industrial water conservation; ΔV S represent The driving effect of ΔVS It is the effect of industrial structure; ΔV I represent The driving effect of ΔV I For water efficiency.

[0055] Based on the LMDI method, the driving effect decomposition model of industrial water withdrawal is obtained, which includes the economic scale effect ΔV G , water-saving improvement effect ΔV P , Industrial structure effect ΔV S and water efficiency effect ΔV I The four driving effect factors are as follows:

[0056]

[0057] (4) The driving effect decomposition model is combined with the decoupling elasticity coefficient model to establish a decoupling index decomposition model between industrial water withdrawal and industrial economic growth. The decoupling index of the driving effect is calculated based on the decoupling index decomposition model.

[0058] Substituting the above formula (3) into formula (1), the expression of the decoupling index decomposition model of industrial water withdrawal and industrial economic growth is as follows:

[0059]

[0060] Where: D P , D G , D S , D I They are water conservation progress effect decoupling index, industrial scale effect decoupling index, industrial structure effect decoupling index and water use efficiency effect decoupling index.

[0061] It should be further explained that the gross national product and industrial added value in this invention are the same concept (both represent GDP). According to the national standard "Terms for Water Conservation" (GB\T 21534-2021), water intake refers to the amount of water obtained from various water sources or channels, including conventional water intake and unconventional water utilization; water consumption is the sum of the water intake of the water use unit and the amount of water reused.

[0062] Example:

[0063] Taking the traditional pillar industry clusters in Henan Province from 2010 to 2016 as an example, the above model and calculation method of the present invention are used to calculate the four driving effect factors ΔV of the inter-annual industrial water withdrawal changes of the industrial clusters by formulas (4) to (7): P , ΔV G , ΔV S , ΔV IThe driving effect of the decoupling is then calculated according to formula (8): P , D G , D S , D I The driving effects of the cluster are decomposed into various industries in the cluster to obtain the total driving effect and contribution of different industries to the change of the industrial water decoupling index of the cluster, as well as the driving effects and contributions of various decoupling indexes of different industries.

[0064] In this embodiment, the traditional pillar industry clusters in Henan Province include coal mining and washing, oil and natural gas mining, ferrous metal mining, non-ferrous metal mining, textiles, petroleum processing, coking and nuclear fuel processing, chemical raw materials and chemical products manufacturing, non-metallic mineral products, ferrous metal smelting and rolling processing, non-ferrous metal smelting and rolling processing, electricity, heat production and supply, and gas production and supply. The industrial water decoupling index change driving model established based on the Tapio decoupling model and the LMDI method is used to calculate the inter-annual decoupling driving effect D of the industrial cluster. P , D G , D S , D I , and then decompose these driving effects into various industries of the cluster to obtain the total driving effect and contribution of different industries to the change of the industrial water decoupling index of the cluster, as well as the driving effects and contributions of various decoupling indexes of different industries.

[0065] Reference Figures 1-2 The driving effect and contribution of the industrial water decoupling index of traditional pillar industry clusters in Henan Province analyzed according to the method of the present invention are shown in Tables 1 and 2.

[0066] Table 1 Results of the driving effect of industrial water decoupling index of traditional pillar industry clusters

[0067] years Total Change Water saving improvement effect Industrial structure effect Water efficiency effect Industrial scale effect 2010-2011 0.517 0.147 -0.087 -0.505 0.962 2011-2012 -1.575 -1.491 -0.525 -0.418 0.860 2012-2013 -0.154 -0.630 -0.191 -0.280 0.948 2013-2014 -0.345 -0.974 -0.420 0.111 0.938 2014-2015 -0.228 -0.856 -0.507 0.171 0.965 2015-2016 -0.415 -2.165 -0.274 1.058 0.967

[0068] Table 2: The contribution of the driving effect of the industrial water decoupling index of traditional pillar industry clusters

[0069] years Water saving improvement effect Industrial structure effect Water efficiency effect Industrial scale effect 2010-2011 13.255 -14.696 -85.304 86.745 2011-2012 -61.257 -21.569 -17.173 100.000 2012-2013 -57.221 -17.348 -25.431 100.000 2013-2014 -69.871 -30.129 10.582 89.418 2014-2015 -62.803 -37.197 15.053 84.947 2015-2016 -88.766 -11.234 52.247 47.753

[0070] As shown in Table 2, among the various decoupling indexes from 2010 to 2016, industrial scale showed a major negative driving effect. After 2013, the negative driving effect of industrial scale decreased significantly, while the negative driving effect of water use efficiency gradually increased. Water-saving progress has always shown a major positive driving effect, and has grown steadily since 2012, with the positive driving contribution rate reaching 88.8% in 2012. The analysis shows that the water-saving progress technology of Henan Province's industry, especially the traditional pillar industries, has steadily improved, with significant results. At the same time, the industrial structure shows a stable positive driving effect, but the contribution is low and fluctuates.

[0071] These driving effects are broken down into the main industries of the cluster, referring to Figures 3-4 The annual total driving effect and contribution of different industries to the changes in the industrial water decoupling index of traditional pillar industry clusters are obtained, as shown in Tables 3 and 4.

[0072] Table 3 The total driving effect of different industries on the changes in the industrial water decoupling index of traditional pillar industry clusters

[0073]

[0074] Table 4 Contribution of different industries to the change of industrial water decoupling index of traditional pillar industry clusters

[0075]

[0076]

[0077] As shown in Table 4, among various industries from 2010 to 2016, the total driving effect of coal mining and washing, non-metallic mineral products, electricity, heat production and supply, chemical raw materials and chemical products manufacturing industries was relatively large, and they were identified as key industries for the changes in the industrial water decoupling index of traditional pillar industry clusters; among them, the electricity, heat production and supply industry generally showed a positive driving effect, but the effect tended to be significantly attenuated; the coal mining and washing industry generally showed a positive driving effect and tended to be a stable growth trend, with a contribution rate of 22.8% from 2015 to 2016.

[0078] According to the method of the present invention, the driving effect of the water decoupling index of each specific industry can be further analyzed in detail. In this embodiment, the driving effect of the decoupling index and its contribution of the production and supply of electricity and heat in the pillar industries of Henan Province from 2010 to 2016 are taken as an example. Figures 5-6 ,The detailed analysis is shown in Table 5 and Table 6.

[0079] Table 5 Driving effect of water use decoupling index in the production and supply of electricity and heat

[0080] years Total Change Water saving improvement effect Industrial structure effect Water efficiency effect Industrial scale effect 2010-2011 0.228 0.440 -0.065 -0.568 0.420 2011-2012 -1.363 -1.980 -0.476 0.758 0.335 2012-2013 0.013 -1.164 -0.191 1.039 0.329 2013-2014 -0.148 -0.085 -0.489 0.100 0.327 2014-2015 -0.288 -0.913 -0.435 0.732 0.329 2015-2016 -0.009 -1.855 -0.238 1.757 0.327

[0081] Table 6 Contribution of driving effect of water use decoupling index in electricity and heat production and supply industries

[0082] years Water saving improvement effect Industrial structure effect Water efficiency effect Industrial scale effect 2010-2011 51.163 -10.269 -89.731 48.837 2011-2012 -80.619 -19.381 69.350 30.650 2012-2013 -85.904 -14.096 75.950 24.050 2013-2014 -14.808 -85.192 23.419 76.581 2014-2015 -67.730 -32.270 68.992 31.008 2015-2016 -88.629 -11.371 84.309 15.691

[0083] As shown in Table 6, the water efficiency driving effect in the electricity and heat production and supply industry from 2010 to 2016 showed a major negative driving effect and tended to grow steadily, with a contribution rate of 84.3% in 2015-2016; while the water-saving progress driving effect showed a major positive driving effect and tended to grow steadily, with a contribution rate of -88.6% in 2015-2016, indicating that the main driving effect of water decoupling in this industry comes from the driving of water-saving progress and water efficiency, which is the main driving factor of water decoupling in this industry and the key direction of water-saving management in this industry. The water efficiency driving effect is the main source of the significant attenuation of the total driving effect of water decoupling in this industry, and its market performance is the increase in costs and the attenuation of economic benefits in this industry.

[0084] It should be noted that the driving effect of the industrial water decoupling index of each industry in Table 2 can be calculated according to the method of the present invention. Tables 5 and 6 only represent the driving effect and contribution of the decoupling index of "electricity, heat production and supply industry", which does not limit the protection scope of the present invention. The driving effect analysis of the industrial water decoupling index based on clusters (regions) and their various industries will help the functional departments of regional water management and industrial industry management to fully understand the current status and problems of water decoupling in a timely manner, introduce targeted countermeasures and measures to meet actual management needs, and have guiding significance for industry management.

[0085] It should be clear that the above detailed description of the embodiments of the present invention provided in the accompanying drawings is only used to further illustrate the present invention and is not intended to limit the scope of the invention claimed for protection, but only represents selected embodiments of the present invention. Based on the embodiments of the present invention, some non-essential improvements and adjustments made by ordinary technicians in this field according to the above content of the present invention belong to the protection scope of the present invention.

Claims

1. A method for calculating the driving effect of regional industrial water decoupling index, characterized in that: The following steps are involved: S1: Based on the Tapio decoupling model, a decoupling elasticity coefficient model between industrial water withdrawal and industrial economic growth is constructed; S2: Use the Kaya identity to determine the factors affecting changes in industrial water withdrawal; S3: The factor decomposition method is used to decompose the influencing factors of industrial water withdrawal changes, and the driving effect decomposition model of industrial water withdrawal is obtained; S4: Combine the driving effect decomposition model with the decoupling elasticity coefficient model to establish a decoupling index decomposition model of industrial water withdrawal and industrial economic growth. Based on the decoupling index decomposition model, calculate the driving effect and contribution of the decoupling index.

2. The method for calculating the driving effect of the regional industrial water decoupling index according to claim 1 is characterized in that: In step S1, the expression of the decoupling elastic coefficient model is: Where: R V,G is the decoupling elasticity coefficient; ΔV is the change in industrial water withdrawal; V is the industrial water withdrawal; ΔG is the change in GDP, and G is the GDP.

3. The method for calculating the driving effect of the regional industrial water decoupling index according to claim 2 is characterized in that: In step S2, the Kaya identity is used to expand and obtain the influencing factors of industrial water withdrawal changes, where the expression of industrial water withdrawal in the tth year is: Where: V t is the industrial water withdrawal in year t; Water intake of the i-th industrial sector in year t; G t is the industrial added value in year t; Industrial added value of the i-th industrial sector in year t; is the industrial water reuse rate of the i-th industrial sector in year t; is the water consumption of the i-th industrial sector in year t; is the ratio of industrial non-reusable water consumption to water consumption of the i-th industrial sector in year t; is the proportion of the added value of the ioth industrial sector in the tth year to the added value of the industry in that year; It is the water consumption per 10,000 yuan of added value of the i-th industrial sector in the t-th year.

4. The method for calculating the driving effect of the regional industrial water decoupling index according to claim 3 is characterized in that: In step S3, the LMDI method is used to decompose the influencing factors of the change in industrial water withdrawal. The LMDI expression of the change in industrial water withdrawal from the base period to the calculation period is as follows: ΔV=V t -V 0 =ΔV P +ΔV G +ΔV S +ΔV I (3) Where: ΔV G Represents G t The driving effect of ΔV is defined as the industrial scale effect based on economic output; p represent The driving effect of It is only closely related to the reuse rate of industrial water, so ΔV is defined p To save water and improve the effect; ΔV s represent The driving effect of ΔV s It is the effect of industrial structure; ΔV i represent The driving effect of ΔV i For water efficiency.

5. The method for calculating the driving effect of the regional industrial water decoupling index according to claim 4 is characterized in that: Based on the LMDI method, the driving effect decomposition model of industrial water withdrawal is obtained as follows:

6. The method for calculating the driving effect of the regional industrial water decoupling index according to claim 5 is characterized in that: In step S4, the expression of the decoupling index decomposition model between industrial water withdrawal and industrial economic growth is as follows: Where: D P , D G , D S , D I They respectively represent the water conservation progress effect decoupling index, industrial scale effect decoupling index, industrial structure effect decoupling index, and water use efficiency effect decoupling index.

7. An electronic device, comprising a processor and a memory, characterized in that: The memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory to implement the steps of a method for calculating the driving effect of a regional industrial water decoupling index as described in any one of claims 1 to 6.

8. A computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable a computer to execute the steps of a method for calculating the driving effect of a regional industrial water decoupling index as described in any one of claims 1 to 6.

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