A method for assessing the impact of climate change on fishery production based on double difference method

Abstract

The invention discloses a method for evaluating the impact of climate change on fishery production based on a double difference method, comprising the following steps: S1, selecting samples and performing data extraction and data preprocessing; S2, determining grouping and impact time points, and constructing double virtual variables of a double difference model; S3, applying the double difference model to test the impact of abnormal ocean warming events on marine aquaculture production; S4, conducting a parallel trend hypothesis test; S5, conducting a robustness test by expanding the scope of the experimental group and using a fictitious experimental group; S6, testing the regulatory effect of sea surface temperature on the impact effect; S7, using a group regression method to perform heterogeneity analysis on key influencing factors; the invention is simple to operate, flexible and easy to use, overcomes the problem of interference of other potential linear or nonlinear factors on the result variable, and brings convenience to the attribution study of the impact of climate change on fisheries.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and analysis, and in particular to a method for evaluating the impact of climate change on fishery output based on a double difference method. Background Art

[0002] In recent years, global climate change caused by population growth and increased industrial activities has received widespread attention. Among them, global warming has caused the global average sea surface temperature to continue to rise, which may have an impact on the fishery economic sector. Since the global sea surface temperature changes relatively slowly and it is difficult to uniformly observe significant changes, it is not easy to examine the specific net impact and direction of ocean warming on fisheries alone; and due to the nonlinear effects of multiple processes interacting, quantitative research on the correlation between fishery production and climate indices often fails, limited by strong linear or nonlinear assumptions. In the study of climate change and economic impact, the commonly used multivariate linear regression model has a limited fitting ability due to the linear assumption and is not suitable for complex situations where the relationship between the dependent variable and the independent variable is nonlinear; although nonlinear regression can handle complex relationships between variables, the model design needs to consider various variables and influence mechanisms that may affect the outcome variable. The fitting results may be affected by the initial estimates of different parameters. The model is relatively complex and there is a risk of overfitting, and the interpretation of results and model testing are difficult. Summary of the invention

[0003] The purpose of the present invention is to provide a method for evaluating the impact of climate change on fishery production based on the double difference method. The method is simple to operate, flexible and easy to use, overcomes the interference problem of other potential linear or nonlinear factors on the result variables, and facilitates the attribution study of the impact of climate change on marine fisheries.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A method for assessing the impact of climate change on fishery production based on the double difference method includes the following steps:

[0006] S1, select samples and perform data extraction and data preprocessing;

[0007] S2, determine the grouping and impact time point, and construct double dummy variables for the double difference model;

[0008] S3. Apply the double difference model to examine the impact of abnormal ocean warming events on marine aquaculture production;

[0009] The specific process of step S3 is: using the double difference method to control individual fixed effects and time fixed effects; the control variables are the economic and social development levels of each country, including per capita GDP, total population and the proportion of added value of the primary industry; the explained variable is the annual output of marine aquaculture in each country; the explanatory variable is whether the sea surface temperature has increased abnormally, which is examined with a non-continuous double dummy variable:

[0010] Introducing grouping dummy variables Treat , test the difference in mariculture production between areas with abnormal sea surface temperature rise and areas without abnormal sea surface temperature rise. If the exclusive economic zone of the sample country where mariculture activities are carried out is located in the central and eastern tropical Pacific Ocean, it is assigned a value of 1, otherwise it is assigned a value of 0;

[0011] Introducing time dummy variables Post , to test the changes in the yield of the experimental group and the control group before and after the abnormal increase in sea surface temperature, the year of the high-intensity El Niño event and the years after it were assigned a value of 1, and the rest of the years were assigned a value of 0;

[0012] The regression equation of the double difference model is: Y it = α ( Post it × Treat it ) + X it + μ i + λ t + ε it in, i represents each sample country, t Indicates the year; Y it represents the explained variable; X it represents control variables that vary over time and across individuals; μ i represents individual fixed effects; λ t represents time fixed effects; ε it is the random error term; the interaction term Post it × Treat itIt represents the treatment variable of abnormal increase of sea surface temperature. The treatment variable of the countries in the experimental group takes the value of 0 before the abnormal increase of sea surface temperature. The treatment variable of the countries in the experimental group takes the value of 1 in the years when the sea surface temperature of the countries in the experimental group increases abnormally and thereafter. The treatment variable of the control group is always 0. α is a coefficient used to represent the treatment effect of abnormal increase in sea surface temperature on the production of marine aquaculture in the experimental group. α The significance level p value is less than 0.05, that is, through the t test, it shows that the abnormal increase in sea surface temperature has a significant impact on the output of marine aquaculture, and the coefficient α The positive or negative value indicates the specific direction of the impact;

[0013] S4. Conduct parallel trend hypothesis test;

[0014] S5. Conduct robustness tests by expanding the scope of the experimental group and using a fictitious experimental group;

[0015] S6. Test the moderating effect of sea surface temperature on the impact;

[0016] S7. Group regression was used to conduct heterogeneity analysis on key influencing factors.

[0017] Preferably, the samples in step S1 include countries or regions around the world that conduct marine fishery activities; the data include abnormal warming of global sea surface temperature, marine aquaculture production in coastal countries around the world, socio-economic data of coastal countries around the world, and vector data of national borders, territorial waters, contiguous zones and exclusive economic zones; the socio-economic data of coastal countries around the world include the per capita GDP, per capita adult national income, proportion of primary industry added value, total population and total labor force of coastal countries around the world.

[0018] Preferably, the data extraction in step S1 comprises the following steps:

[0019] S11. Data extraction for different variables: Data extraction includes geographic information data extraction, sea surface temperature data extraction and marine aquaculture production data extraction; among them, geographic information uses the boundaries of the exclusive economic zones of various countries as the largest area where marine aquaculture fishery activities may be carried out, and the longitude and latitude geographic coordinates of its boundaries are extracted using ArcGIS software; the sea surface temperature data are collected from the data products of the National Oceanic and Atmospheric Administration of the United States, taking the annual average value of the sea surface temperature, and based on the longitude and latitude geographic coordinates of the boundaries of the sea areas where the countries may carry out marine aquaculture fishery activities, the average value of the sea surface temperature within the exclusive economic zones of various countries is extracted to reflect the sea surface temperature conditions in the sea areas where the countries are located; marine aquaculture data are collected from the statistical data of the Fisheries and Aquaculture Division of the Food and Agriculture Organization of the United Nations, excluding the data marked as coming from inland waters in the production data of various countries, and merging the production of different species;

[0020] S12. Unify the time scale and spatial scale of all data: the time scale is calculated based on years, and the spatial scale is calculated based on countries.

[0021] Preferably, the data preprocessing in step S1 includes screening and retaining sample country data, eliminating missing data, matching different variable objects, data format conversion and logarithmic processing of continuous variables to form panel data in a long data format containing multivariate variables.

[0022] Preferably, the specific process of determining the grouping and impact time point in step S2 is: determining with the help of the exogenous event El Niño that affects the sea surface temperature, based on the positive anomaly of the sea surface temperature in the central and eastern tropical Pacific caused by the El Niño event, referring to the abnormally high sea temperature anomaly index in the NINO comprehensive area of ​​the National Climate Center of China as a characterization of the occurrence of the El Niño event, selecting countries in the tropical central and eastern Pacific region with a longitude of 160°E-180°-80°W and a latitude of 23.5°N-23.5°S as the experimental group, and the remaining countries as the control group; selecting the year when a high-intensity El Niño event occurs as the impact time point of abnormal ocean warming, and selecting the year with a certain time interval between the two low-intensity El Niño events as the research time interval, which is used to construct the double dummy variables of the double difference model to study the impact of sea temperature changes on marine aquaculture.

[0023] Preferably, the equation for the parallel trend hypothesis test in step S4 is: in, D it A dummy variable indicating abnormal increase in sea surface temperature during the current period. i The sea area is located in t If the annual sea surface temperature rises abnormally, the value is 1, otherwise it is 0; the meanings of the other variables are the same as those in the regression equation of the double difference model; is a coefficient used to represent the abnormal increase in sea surface temperature in the experimental group countries. t Differences in annual production changes of marine aquaculture between the experimental group and the control group during the year.

[0024] Preferably, the robustness test in step S5 comprises the following steps:

[0025] S51. Expand the scope of experimental group selection, conduct double difference model regression, and test the regression coefficient of interaction term α Whether it is significant; expand the latitude range of the experimental group to between 30° north and south latitude and between 40° north and south latitude for model regression in turn to avoid the limited reliability of the model regression results due to the small number of experimental groups and the subjectivity of the grouping range;

[0026] S52. Take other oceanic countries at the same latitude as the experimental group, conduct model regression, and test the regression coefficient of the interaction term α Whether it is significant, to examine whether the choice of the experimental group in the central and eastern Pacific Ocean is reasonable;

[0027] S53. Randomly select a pseudo-experimental group and perform model regression to test whether the regression coefficient of the interaction term is significant.

[0028] Preferably, the regression equation for testing the moderating effect of sea surface temperature on the impact effect in step S6 is: Y it = β ( Post it × Treat it × ln ( SST it )) + X it + μ i + λ t + ε it Among them, ln ( SST it ) indicates country i The sea area is located in t The logarithm of the annual average sea surface temperature; the meanings of the other variables are the same as those in the regression equation of the double difference model; β is a coefficient used to represent the moderating effect of the sea surface temperature of the experimental group on the impact of the abnormal ocean warming event based on the El Niño phenomenon, in order to test the specific role of the sea surface temperature factor in the impact of the abnormal climate event El Niño on marine fisheries; if the coefficient is tested, β If the significance level p value is less than 0.05, it indicates that the sea surface temperature has a significant effect on the impact effect. β The interaction coefficient in the double difference model α The positive and negative signs of are the same, which indicates that the sea surface temperature has strengthened the impact of the aforementioned abnormal ocean warming event, that is, the higher the sea surface temperature of the experimental group, the greater the impact on its marine fisheries; if the coefficient β The interaction coefficient in the double difference model α If the signs of are opposite, it means that the sea surface temperature weakens the impact effect, that is, the higher the sea surface temperature in the experimental group, the smaller the impact on its marine fisheries.

[0029] Preferably, the process of heterogeneity analysis in step S7 is: examine the differential impact of ocean warming on the output of marine aquaculture in countries with different socioeconomic levels, select different indicators that can reflect the economic development status, population conditions, and industrial structure of each country, group the sample countries, perform double difference model regression again, and compare the interaction coefficients of different groups. α difference.

[0030] After adopting the above technical solution, the present invention has the following beneficial effects:

[0031] 1. The present invention adopts a causal inference method based on a double difference model. The double difference method is often used to evaluate the average treatment effect of external shock events on the experimental group. By calculating the difference in the incremental results caused by the experimental group and the control group under the intervention in the observed data, the causal relationship is verified to obtain the net effect of the exogenous intervention. The double difference model can eliminate the influence of other potential factors on the dependent variable by controlling the fixed effects of time and individuals, and can avoid endogeneity problems to a large extent, thereby exploring the causal relationship between variables, rather than being limited to correlation, and does not require assumptions based on linear or nonlinear relationships. The model is scientific, simple, robust and reliable, and has broad application prospects.

[0032] 2. The study on the impact of climate change on fisheries provided by the present invention adopts a double difference model. By constructing double virtual variables of individuals and time to describe the abnormal regional ocean warming on a global scale, and fixing the individual fixed effect and the time fixed effect at the same time, the interference of potential factors that are difficult to observe and affect marine fisheries on the estimation of the degree of impact can be eliminated; by using the exogenous event El Niño to determine the regions and time nodes where abnormal changes in sea surface temperature occur, the net impact of ocean warming on marine aquaculture production in different countries is determined, providing technical support for the study of the impact of climate change on marine fisheries. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a flow chart of the present invention;

[0034] Figure 2 It is a parallelism trend test result diagram of the present invention;

[0035] Figure 3 This is a diagram of the placebo test results of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0037] Reference Figure 1The present invention proposes a method for evaluating the impact of climate change on fishery production based on the double difference method. The method takes the abnormal sea temperature rise event in the tropical central and eastern Pacific caused by the super El Niño as the research object, examines the impact of abnormal ocean warming on marine aquaculture production, and explores the degree of heterogeneous influence of socioeconomic factors, including the following steps:

[0038] S1. Select samples and perform data extraction and data preprocessing. The variable settings used are shown in Table 1.

[0039] Table 1 Variable settings

[0040]

[0041] S2. Determine the grouping and impact time point. Select 2011-2018 as the research time interval. The interval includes a super El Niño event (2014-2016 El Niño). The impact time point of the abnormal ocean warming event is 2014. Descriptive statistics of variables are performed for the experimental group and the control group, as shown in Table 2.

[0042] Table 2 Descriptive statistics of variables

[0043]

[0044] S3. The impact of abnormal ocean warming events on marine aquaculture production was tested, and the double difference model regression results are shown in Table 3.

[0045] Table 3 Regression results of sea surface temperature increase and marine aquaculture production

[0046]

[0047] Note: * , ** , *** Represents significance at the 10%, 5%, and 1% levels, respectively; the numbers in brackets represent robust standard errors.

[0048] According to the regression results of the double difference model, in column (1), only the explanatory variables and the explained variables are examined, and the individual and year effects are fixed. Post × Treat The regression coefficient is -0.661, which is significantly negative at the 1% level, indicating that the increase in sea surface temperature in the central and eastern tropical Pacific Ocean caused by the high-intensity El Niño event that began in 2014 will lead to a significant decrease in the production of marine aquaculture in the countries in the region. After adding three control variables of per capita GDP, total population, and proportion of primary industry in socio-economic development factors, Post × TreatThe regression coefficient is -0.614, which is significantly negative at the 1% level. The conclusion that rising sea surface temperature has a significant negative impact on marine aquaculture production still holds true.

[0049] S4. Perform a parallel trend hypothesis test. The results are as follows Figure 2 shown.

[0050] like Figure 2 Before the impact of the high-intensity El Niño event in 2014, which caused the sea surface temperature to rise in the central and eastern tropical Pacific, there was no significant difference in the marine aquaculture production between the experimental group and the control group, indicating that the parallel trend test was met; and from the second year after the impact, the marine aquaculture production between the experimental group and the control group gradually showed a significant difference, indicating that the increase in sea surface temperature had a significant impact on the production of marine aquaculture. Among them, Figure 2 Current indicates the time point when the ocean abnormal warming shock occurred, i.e. 2014; pre_3 indicates 2011 before the shock; pre_2 indicates 2012 before the shock; post_1 indicates 2015 after the shock; post_2 indicates 2016 after the shock; post_3 indicates 2017 after the shock; post_4 indicates 2018 after the shock. The black dotted line indicates the 90% confidence interval: if it crosses the 0 scale line on the y-axis, it indicates that the impact of the shock is not significant; if the 90% confidence interval is far from the 0 scale line, it indicates that the shock has a significant impact.

[0051] S5. Perform a robustness test, which specifically includes the following steps:

[0052] S51. The experimental group was expanded to conduct sensitivity analysis. The regression results are shown in Table 4.

[0053] Table 4 Sensitivity analysis regression results

[0054]

[0055] Note: * , ** , *** Represents significance at the 10%, 5%, and 1% levels, respectively; the numbers in brackets represent robust standard errors.

[0056] According to the regression results of the sensitivity analysis, when a high-intensity El Niño event occurs, there may also be a certain degree of abnormal sea surface temperature in the waters near the central and eastern tropical Pacific Ocean. This abnormal change in sea surface temperature will cause a decline in local marine aquaculture production, and its net effect will cause an average decline of about 50%-55% in the production of the experimental group countries, which is still a large impact.

[0057] S52. Taking other oceanic countries at the same latitude as the experimental group, model regression was conducted. The test results are shown in Table 5.

[0058] Table 5 Regression results of the reset experimental group

[0059]

[0060] According to the regression results of the re-setting experimental group, the results in column (1) are the regression results of only taking the tropical Atlantic Ocean, Indian Ocean, and western Pacific Ocean as the experimental group, and the interaction terms Post × Treat The coefficient is positive and not statistically significant. The results in column (2) are obtained by combining the original experimental group with the new experimental group, that is, including all tropical countries in the experimental group for regression. Post × Treat The coefficient is negative and not significant. Therefore, when the super El Niño event occurs, the marine aquaculture fishery output of countries in other tropical ocean regions will not change significantly, indicating that the experimental group of the original regression model is reasonably selected and the significant conclusion can be trusted.

[0061] S53, randomly select pseudo-experimental groups, perform model regression, and test the results. Figure 3 shown.

[0062] according to Figure 3 In the placebo test of the randomly sampled fictitious experimental group, the interaction term coefficient of 500 random sampling regressions was close to the normal distribution, far away from the interaction term regression coefficient of the original model. The regression coefficients of most pseudo-experimental groups were concentrated around 0, and the closer to 0, the greater the density. Most of the p-values ​​were above 10%, that is, the regression coefficient was not significant, indicating that the randomly set pseudo-experimental group was basically not substantially affected by ocean warming.

[0063] S6. The moderating effect of sea surface temperature on the impact is tested. The regression results are shown in Table 6.

[0064] Table 6 Regression results of the moderating effect of sea surface temperature

[0065]

[0066] According to the regression results of the adjustment effect, the sea surface temperature and the original double difference model Post × Treat The coefficient of the interaction term is significant at the 1% level, indicating that the sea surface temperature has a very significant impact on the impact of the abnormal ocean warming event based on the El Niño phenomenon; and this coefficient is consistent with the original double difference model. Post × Treat The regression coefficients are all negative, indicating that the sea surface temperature strengthens the impact effect. The higher the sea surface temperature, the greater the negative impact on marine aquaculture.

[0067] S7. Group regression was used to conduct heterogeneity analysis on socioeconomic factors, and the regression coefficients of interaction terms are shown in Table 7.

[0068] Table 7 Heterogeneity analysis group regression interaction coefficients

[0069]

[0070] According to the results of heterogeneity analysis group regression, the interaction coefficients of all group regressions are significant; according to the size of the interaction coefficients of each group, it is found that in countries with low income levels, small population and labor resources, and economic structures that are highly dependent on primary industries such as fisheries, marine aquaculture production is more negatively affected by abnormal ocean warming.

[0071] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for assessing the impact of climate change on fishery production based on the double difference method, characterized in that: The following steps are involved: S1, select samples and perform data extraction and data preprocessing; S2, determine the grouping and impact time point, and construct double dummy variables for the double difference model; S3. Apply the double difference model to examine the impact of abnormal ocean warming events on marine aquaculture production; The specific process of step S3 is: using the double difference method to control individual fixed effects and time fixed effects; the control variables are the economic and social development levels of each country, including per capita GDP, total population and the proportion of added value of the primary industry; the explained variable is the annual output of marine aquaculture in each country; the explanatory variable is whether the sea surface temperature has increased abnormally, which is examined by a non-continuous double dummy variable; Introducing grouping dummy variables Treat , test the difference in mariculture production between areas with abnormal sea surface temperature rise and areas without abnormal sea surface temperature rise. If the exclusive economic zone of the sample country where mariculture activities are carried out is located in the central and eastern tropical Pacific Ocean, it is assigned a value of 1, otherwise it is assigned a value of 0; Introducing time dummy variables Post , to test the changes in the yield of the experimental group and the control group before and after the abnormal increase in sea surface temperature, the year of the high-intensity El Niño event and the years after it were assigned a value of 1, and the rest of the years were assigned a value of 0; The regression equation of the double difference model is: Y it = α ( Post it × Treat it ) + X it + μ i + λ t + ε it in, i represents each sample country, t Indicates the year; Y it represents the explained variable; X it represents control variables that vary over time and across individuals; μ i represents individual fixed effects; λ t represents time fixed effects; ε it is the random error term; the interaction term Post it × Treat it It represents the treatment variable of abnormal increase of sea surface temperature. The treatment variable of the countries in the experimental group takes the value of 0 before the abnormal increase of sea surface temperature. The treatment variable of the countries in the experimental group takes the value of 1 in the years when the sea surface temperature of the countries in the experimental group increases abnormally and thereafter. The treatment variable of the control group is always 0. α is a coefficient used to represent the treatment effect of abnormal increase in sea surface temperature on the production of marine aquaculture in the experimental group. α The significance level p value is less than 0.05, that is, through the t test, it shows that the abnormal increase in sea surface temperature has a significant impact on the output of marine aquaculture, and the coefficient α The positive or negative value indicates the specific direction of the impact; S4, conduct parallel trend hypothesis test; S5. Conduct robustness tests by expanding the scope of the experimental group and using a fictitious experimental group; S6. Test the moderating effect of sea surface temperature on the impact; The regression equation for testing the moderating effect of sea surface temperature on the impact effect in step S6 is: Y it = β ( Post it × Treat it × ln ( SST it )) + X it + μ i + λ t + ε it Among them, ln ( SST it ) indicates country i The sea area is located in t The logarithm of the annual average sea surface temperature; the meanings of the other variables are the same as those in the regression equation of the double difference model; β is a coefficient used to represent the moderating effect of the sea surface temperature of the experimental group on the impact of the abnormal ocean warming event based on the El Niño phenomenon, in order to test the specific role of the sea surface temperature factor in the impact of the abnormal climate event El Niño on marine fisheries; if the coefficient is tested, β If the significance level p value is less than 0.05, it indicates that the sea surface temperature has a significant effect on the impact effect. β The interaction coefficient in the double difference model α The positive and negative signs of are the same, which indicates that the sea surface temperature has strengthened the impact of the aforementioned abnormal ocean warming event, that is, the higher the sea surface temperature of the experimental group, the greater the impact on its marine fisheries; if the coefficient β The interaction coefficient in the double difference model α The opposite signs of indicate that the sea surface temperature weakens the impact effect, that is, the higher the sea surface temperature in the experimental group, the smaller the impact on its marine fisheries. S7. Group regression was used to conduct heterogeneity analysis on key influencing factors.

2. The method for evaluating the impact of climate change on fishery production based on the double difference method as claimed in claim 1, characterized in that: The samples in step S1 include countries or regions around the world that conduct marine fishery activities; the data include abnormal warming of global ocean surface temperature, marine aquaculture production in coastal countries around the world, socio-economic data of coastal countries around the world, and vector data of national borders, territorial waters, contiguous zones and exclusive economic zones; the socio-economic data of coastal countries around the world include per capita GDP, per capita adult national income, proportion of primary industry added value, total population and total labor force of coastal countries around the world.

3. The method for evaluating the impact of climate change on fishery production based on the double difference method as claimed in claim 1, characterized in that: The data extraction in step S1 includes the following steps: S11. Data extraction for different variables: Data extraction includes geographic information data extraction, sea surface temperature data extraction and marine aquaculture production data extraction; among them, geographic information uses the boundaries of the exclusive economic zones of various countries as the largest area where marine aquaculture fishery activities may be carried out, and the longitude and latitude geographic coordinates of its boundaries are extracted using ArcGIS software; the sea surface temperature data are collected from the data products of the National Oceanic and Atmospheric Administration of the United States, taking the annual average value of the sea surface temperature, and based on the longitude and latitude geographic coordinates of the boundaries of the sea areas where the countries may carry out marine aquaculture fishery activities, the average value of the sea surface temperature within the exclusive economic zones of various countries is extracted to reflect the sea surface temperature conditions in the sea areas where the countries are located; marine aquaculture data are collected from the statistical data of the Fisheries and Aquaculture Division of the Food and Agriculture Organization of the United Nations, excluding the data marked as coming from inland waters in the production data of various countries, and merging the production of different species; S12. Unify the time scale and spatial scale of all data: the time scale is calculated based on years, and the spatial scale is calculated based on countries.

4. The method for evaluating the impact of climate change on fishery production based on the double difference method as claimed in claim 1, characterized in that: The data preprocessing in step S1 includes screening and retaining sample country data, eliminating missing data, matching different variable objects, data format conversion and logarithmic processing of continuous variables to form panel data in a long data format containing multivariate variables.

5. The method for evaluating the impact of climate change on fishery production based on the double difference method as claimed in claim 1, characterized in that: The specific process of determining the grouping and impact time point described in step S2 is: using the exogenous event El Niño that affects the sea surface temperature to determine, based on the positive anomaly of the sea surface temperature in the central and eastern tropical Pacific caused by the El Niño event, referring to the abnormally high sea temperature anomaly index in the NINO comprehensive area of ​​the National Climate Center of China as a representation of the occurrence of the El Niño event, the countries in the tropical central and eastern Pacific with longitudes of 160°E-180°-80°W and latitudes of 23.5°N-23.5°S are selected as the experimental group, and the remaining countries are selected as the control group; the year when the high-intensity El Niño event occurs is selected as the impact time point of abnormal ocean warming, and the year with a certain time interval between the two low-intensity El Niño events before and after is selected as the research time interval, which is used to construct the double dummy variables of the double difference model to study the impact of sea temperature changes on marine aquaculture.

6. A method for assessing the impact of climate change on fishery production based on double difference method as claimed in claim 1, characterized in that: The equation for the parallel trend hypothesis test in step S4 is: in, D it A dummy variable indicating abnormal increase in sea surface temperature during the current period. i The sea area is located in t If the annual sea surface temperature rises abnormally, the value is 1, otherwise it is 0; the meanings of the other variables are the same as those in the regression equation of the double difference model; is a coefficient used to represent the abnormal increase in sea surface temperature in the experimental group countries. t Differences in annual production changes of marine aquaculture between the experimental group and the control group during the year.

7. A method for assessing the impact of climate change on fishery production based on double difference method as claimed in claim 1, characterized in that: The robustness test in step S5 includes the following steps: S51. Expand the scope of experimental group selection, conduct double difference model regression, and test the regression coefficient of interaction term α Whether it is significant; expand the latitude range of the experimental group to between 30° north and south latitude and between 40° north and south latitude for model regression in turn to avoid the limited reliability of the model regression results due to the small number of experimental groups and the subjectivity of the grouping range; S52. Take other oceanic countries at the same latitude as the experimental group, conduct model regression, and test the regression coefficient of the interaction term α Whether it is significant, to examine whether the choice of the experimental group in the central and eastern Pacific Ocean is reasonable; S53. Randomly select a pseudo-experimental group and perform model regression to test whether the regression coefficient of the interaction term is significant.

8. The method for evaluating the impact of climate change on fishery production based on double difference method as claimed in claim 1, characterized in that: The process of heterogeneity analysis in step S7 is: examine the differential impact of ocean warming on the output of marine aquaculture in countries with different socioeconomic levels, select different indicators that can reflect the economic development status, population conditions, and industrial structure of each country, group the sample countries, perform double difference model regression again, and compare the interaction coefficients of different groups α difference.

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