How climate change adaptation strategies and climate migration interact to control food insecurity?

doi:10.1016/j.regsus.2025.100229

Abstract

Abstract:

As the impact of climate change intensifies, climate migration (climate change-induced migration) has become a pressing global issue that requires effective adaptation strategies to lessen its effects. Therefore, this study delved into the complex relationship between climate change adaptation strategies and climate migration with food insecurity serving as a mediating factor. We collected sample data through face-to-face interviews in Khorramabad City, Iran from February to May in 2023. Using the Structural Equation Modeling (SEM), we explored how food insecurity influences the relationship between climate change adaptation strategies and climate migration. The findings showed that while climate change adaptation strategies can boost community resilience, their success is closely tied to levels of food insecurity. About 78.72% of the surveyed households experienced certain levels of food insecurity, increasing the risk of displacement due to climate-related disasters. Climate change adaptation strategies including economic strategies, irrigation management strategies, organic-oriented strategies, sustainable development-oriented strategies, and crop variety management strategies played a significant role in reducing climate migration. Moreover, we found that climate change adaptation strategies not only impact food security, but also shape migration decisions. This research underscores the importance of an integrated approach that links climate change adaptation strategies, climate migration, and food insecurity. This study emphasizes the importance of food security for formulating sustainable adaptation strategies.

Key words: Climate change, Climate migration, Food insecurity, Structural Equation, Modeling (SEM), Khorramabad City

Mohammad Reza PAKRAVAN-CHARVADEH, Jeyran CHAMCHAM, Rahim MALEKNIA. How climate change adaptation strategies and climate migration interact to control food insecurity?[J]. Regional Sustainability, 2025, 6(3): 100229.

Figures/Tables 9

Fig. 1.

Table 1

Table 2

Descriptive statistics of climate migration, food insecurity, and climate change adaptation strategies."

Factor		Item	Factor loading					Descriptive statistic			VIF
Factor		Item	Original sample	Sample mean	SD	t-statistics	P-value	Mean	Average	CV	VIF
Climate migration		I am willing to move to another region	0.867	0.868	0.027	32.148	0.001	1.661	1.738	0.016	2.282
		I feel prepared to migrate if necessary	0.872	0.868	0.031	28.000	0.001	1.731		0.018	2.254
		I would be willing to leave my community	0.937	0.937	0.009	104.111	0.001	1.823		0.005	3.326
Food insecurity		Worrying about food availability	0.588	0.590	0.087	6.782	0.001	1.194	0.804	0.073	1.337
		Preferred food	0.832	0.825	0.027	30.556	0.001	0.961		0.023	2.371
		Limited variety of food	0.811	0.807	0.030	26.900	0.001	0.990		0.030	2.650
		Unwanted food	0.846	0.841	0.028	30.036	0.001	0.951		0.029	3.504
		Smaller meals	0.830	0.826	0.026	31.769	0.001	0.884		0.029	3.321
		Fewer meals	0.764	0.764	0.033	23.152	0.001	0.693		0.048	2.215
		Food shortage	0.701	0.704	0.052	13.538	0.001	0.561		0.093	2.387
		Going to bed hungry	0.643	0.645	0.061	10.574	0.001	0.470		0.130	2.645
		Full day without eating	0.462	0.465	0.078	5.962	0.001	0.540		0.144	1.724
Climate change adaptation strategies	Economic strategies	Non-agricultural activity outside the farm	0.946	0.929	0.133	6.985	0.001	1.772	1.763	0.075	3.586
		Non-agricultural employment (labor, sales, etc.)	0.916	0.902	0.118	7.644	0.001	1.852		0.064	3.771
		Getting a loan	0.915	0.899	0.123	7.309	0.001	1.871		0.066	3.323
		Using personal savings	0.917	0.902	0.119	7.580	0.001	1.724		0.069	3.130
		Reducing household expenses	0.944	0.924	0.143	6.462	0.001	1.671		0.086	3.681
		Livestock sale	0.850	0.831	0.127	6.543	0.001	1.692		0.075	2.823
	Irrigation management strategies	Changing the irrigation system	0.814	0.812	0.030	27.067	0.001	1.750	1.705	0.017	1.742
		Improving the coverage of water transmission channels	0.787	0.781	0.034	22.971	0.001	1.700		0.020	1.692
		Use of alternative water sources (rainwater, sewage, etc.)	0.538	0.532	0.076	7.000	0.001	1.651		0.046	1.200
		Management of irrigation intervals	0.712	0.712	0.052	13.692	0.001	1.832		0.028	1.414
		Watershed management activities (dam, gabion, trust, etc.)	0.513	0.512	0.074	6.919	0.001	1.594		0.046	1.151
	Organic-oriented strategies	Weed management	0.745	0.740	0.047	15.745	0.001	2.131	1.778	0.022	1.565
		Use of organic fertilizer	0.774	0.772	0.036	21.444	0.001	1.630		0.022	1.556
		Organic farming	0.794	0.794	0.026	30.538	0.001	1.640		0.016	1.632
		Diversifying crops	0.864	0.863	0.024	35.958	0.001	1.711		0.014	2.283
	Sustainable development -oriented strategies	Changing the crop cultivation deadline	0.719	0.716	0.046	15.565	0.001	1.583	1.597	0.029	1.592
		Conservation tillage	0.729	0.724	0.044	16.455	0.001	1.684		0.026	1.780
		Agricultural land leveling	0.739	0.734	0.047	15.617	0.001	1.910		0.025	1.777
		Changing the time of crop harvest	0.546	0.540	0.063	8.571	0.001	1.590		0.040	1.301
		Multi-cropping	0.813	0.811	0.025	32.440	0.001	1.570		0.016	2.012
		Compliance with crop rotation	0.747	0.743	0.042	17.690	0.001	1.114		0.038	1.701
		Reducing the distance between crop rows	0.656	0.653	0.052	12.558	0.001	1.732		0.030	1.604
	Crop variety management strategies	High-yielding varieties	0.682	0.686	0.045	15.244	0.001	1.651	1.624	0.027	1.235
		Using cold-resistant and pest-resistant varieties	0.809	0.806	0.033	24.424	0.001	1.692		0.020	1.757
		Using varieties resistant to drought	0.778	0.772	0.040	19.300	0.001	1.612		0.025	1.702
		Using varieties resistant to salt	0.819	0.816	0.026	31.385	0.001	1.544		0.017	1.721

Table 2

Table 3

Table 4

Table 5

Table S1

The description of all statistics for assessing the reliability and validity of the Structural Equation Modeling (SEM)."

Statistic	Description	Equation	Explanation
λ	It describes the correlation between an observed variable and underlying latent variable.	$\lambda =\frac{\text{Cov}(X,Y)}{\sqrt{\text{Var}(X)\times \text{Var}(Y)}}$	λ is the factor loading (Pearson correlation coefficient); Cov(X, Y) is the covariance between observed variable (X) and latent variable (Y); Var(X) is the variance of X; and Var(Y) is the variance of Y. The range of λ is between -1.000 and 1.000. λ value above 0.500 is considered acceptable relationship; λ value ranging from 0.400 to 0.500 is generally considered to reflect moderate relationship; while λ value below 0.400 may indicate weak relationship.
SRMR	It is calculated by the square root of the difference between the observed and predicted covariance matrices.	$\text{SRMR}=\sqrt{\frac{\sum\nolimits_{i=1}^{n}{{{({{O}_{ii}}-{{P}_{ii}})}^{2}}}}{n}}$	SRMR is the Standardized Root Mean Square Residual; i is the observation value; n is the total number of observations; O_ii is the observed covariance matrix; and P_ii is the predicted covariance matrix. The range of SRMR is between 0.000 and 1.000, and SRMR value less than 0.080 is generally considered a good fitting.
d_ULS	It is the sum of squared differences between the observed and predicted covariance matrices.	${{\text{d}}_{\text{ULS}}}=\sum\limits_{i=1}^{n}{\sum\limits_{j=1}^{m}{{{({{O}_{ij}}-{{P}_{ij}})}^{2}}}}$	d_ULS is the Squared Euclidean distance; m is the total number of variables; O_ij is the observed matrix for the observation i^th and variable j^th; and P_ij is the predicted matrix for the observation i^th and variable j^th. There is no fixed range for d_ULS, but lower value indicates a better fitting.
d_G	It is similar to d_ULS, but it takes into account the degrees of freedom.	${{\text{d}}_{\text{G}}}=\sum\limits_{i=1}^{n}{\sum\limits_{j=1}^{m}{\frac{{{({{O}_{ij}}-{{P}_{ij}})}^{2}}}{\delta _{ij}^{2}}}}$	d_G is the Geodesic Distance; and δ2 ij is the variance associated with the observed matrix O_ij. Like d_ULS, lower values indicate a better fitting, with no specific upper limit.
χ²	It is a statistical test used to determine if there is a significant association between categorical variables.	${{\chi }^{2}}=\sum\limits_{l=1}^{L}{\frac{{{({{O}_{l}}-{{E}_{l}})}^{2}}}{{{E}_{l}}}}$	χ² is the Chi-Square; L is the total number of categories; O_l is the observed frequency (count) for the l^th category; and E_l is the expected frequency (count) for the l^th category. The range of χ² is between 0.000 to infinity. Compared with the critical value based on the degrees of freedom, a lower value indicates a better fitting.
NFI	It compares the fitting of a proposed model to a baseline model, which usually represents a model of independence (i.e., no relationship between the variables).	$\text{NFI}=\frac{\chi _{\text{null}}^{2}-\chi _{\bmod \text{el}}^{2}}{\chi _{\text{null}}^{2}}$	NFI is the Normed Fit Index; χ2 nul is the Chi-Square for the null model; and χ2 model is the Chi-Square for the specified model. The range of NFI is from 0.000 to 1.000. NFI value above 0.900 is typically considered indicative of a good fitting.
VIF	It quantifies how much the variance of an estimated regression coefficient increases when predictors are correlated. Moreover, it assesses multicollinearity in regression analysis.	$\text{VIF}=\frac{1}{1-R_{j}^{2}}$	VIF is the Variance Inflation Factor; and R2 j is the coefficient of determination obtained by regressing the j^th independent variable against all other variables. VIF=1.000 means no correlation; 5.000<VIF<10.000 means moderate correlation; 1.000≤VIF≤5.000 means high correlation; and VIF>10.000 means very high correlation.
CV	It measures the relative variability of a dataset compared to its mean.	$\text{CV}=\frac{\sigma }{\mu }$	CV is the coefficient of variation; σ is the standard deviation (SD); and μ is the mean of the dataset. A higher CV indicates greater variability relative to the mean.
RMS	It measures the average of the squared differences (residuals) between the observed and predicted covariance matrices.	$\text{RMS}=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{{{({{O}_{i}}-{{P}_{i}})}^{2}}}}$	Where RMS is the Root Mean Square; O_i is the observed value for the i^th observation; and P_i is the predicted value for the i^th observation. A value of RMS closer to 0.000 suggests that the model adequately captures the relationships in the data.
α	It measures the internal consistency and assesses how closely related a set of items are as a group.	$\alpha =\frac{k}{k-1}(1-\frac{\sum\nolimits_{g=1}^{k}{\delta _{{{Y}_{g}}}^{2}}}{\delta _{Y}^{2}})$	α is the Cronbach’s Alpha; k is the number of items; δ2 Yg is the variance of the g^th item; and δ2 Y is the variance of the total score. The range of α is between 0.000 and 1.000, with values above 0.700 generally being considered acceptable.
ρ_A	It is another measure of internal consistency and is often preferred over Cronbach’s Alpha, because it does not assume equal item loadings.	${{\rho }_{A}}=\frac{\sum{\lambda _{g}^{2}}}{\sum{{{\lambda }_{g}}^{2}}+\sum{\lambda \sigma _{{{\varepsilon }_{g}}}^{2}}}$	ρ_A is the Dillon-Goldstein’s Rho; λ_g is the factor loading of item; and $\sigma _{{{\varepsilon }_{g}}}^{2}$ is the error variance of item. Similar to Cronbach’s Alpha, values of ρ_A above 0.700 are considered acceptable.
CR	It reflects the factors’ reliability based on the factor loadings and error variances.	$\text{CR}=\frac{{{\left( \sum{{{\lambda }_{g}}} \right)}^{2}}}{{{\left( \sum{{{\lambda }_{g}}} \right)}^{2}}+\sum{\sigma _{{{\varepsilon }_{g}}}^{2}}}$	CR is the Composite Reliability. CR value above 0.700 is considered acceptable.
AVE	It indicates how much of the variance in the observed variables (factors) is accounted for by the latent variables.	$\text{AVE}=\frac{\sum\nolimits_{g=1}^{k}{\lambda _{g}^{2}}}{k}$	AVE is the Average Variance Extracted. AVE≥0.500 indicates good convergent validity and AVE<0.500 indicates concerns about the factor’s validity.

Table S1

Table S2

Table S3

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Category	Question	Never (%)	Rarely (%)	Sometime (%)	Often (%)
Worrying about food availability	Q1: worrying about food availability	38.21	30.11	17.19	14.49
Preferred food	Q2: unable to eat preferred food	41.62	29.41	21.57	7.40
Limited variety of food	Q3: eating a limited variety of food	43.21	26.02	20.28	10.49
Unwanted food	Q4: eating foods that you have to eat unappealing food	42.93	26.01	24.00	7.06
Smaller meals	Q5: eating a smaller meal	45.93	27.02	19.87	7.18
Fewer meals	Q6: eating fewer meals in a day	57.48	20.88	16.92	4.72
Food shortage	Q7: no food to eat at home	65.21	18.58	11.51	4.70
Hunger at bedtime	Q8: going to bed hungry at night	70.33	16.51	9.06	4.10
Whole-day without any food	Q9: not eating for a whole day and night	68.57	15.18	10.13	6.12

Criterion	Saturated model	Estimated model
SRMR	0.068	0.068
Squared Euclidean distance	3.427	3.427
Geodesic Distance	1.082	1.082
Chi-Square	1749.170	1749.170
NFI	0.961	0.961
RMS	-	0.122

Factor		Cronbach’s Alpha	Dillon-Goldstein’s Rho	CR	AVE
Climate migration		0.872	0.880	0.922	0.797
Food insecurity		0.882	0.940	0.904	0.522
Climate change adaptation strategies	Economic strategies	0.961	1.002	0.969	0.837
	Irrigation management strategies	0.706	0.742	0.810	0.568
	Organic-oriented strategies	0.805	0.807	0.873	0.633
	Sustainable development-oriented strategies	0.834	0.846	0.876	0.506
	Crop variety management strategies	0.775	0.779	0.856	0.599

Factor		Effect pathway	Original sample	Sample mean	SD	t-statistic	P-value
Economic strategies	Climate migration	Direct effect	-0.046	-0.048	0.009	4.721	0.000
		Indirect effect	-0.008	-0.008	0.001	4.709	0.000
		Total effect	-0.054	-0.040	0.008	4.580	0.000
	Food insecurity	Direct effect	-0.060	-0.064	0.012	4.818	0.000
Irrigation management strategies	Climate migration	Direct effect	-0.201	-0.209	0.079	2.542	0.011
		Indirect effect	-0.026	-0.027	0.007	3.515	0.000
		Total effect	-0.227	-0.236	0.082	2.770	0.006
	Food insecurity	Direct effect	-0.192	-0.200	0.084	2.277	0.023
Organic-oriented strategies	Climate migration	Direct effect	-0.051	-0.045	0.013	3.790	0.000
		Indirect effect	-0.024	-0.024	0.006	3.559	0.000
		Total effect	-0.075	-0.073	0.022	3.225	0.000
	Food insecurity	Direct effect	-0.177	-0.179	0.049	3.554	0.000
Sustainable development-oriented strategies	Climate migration	Direct effect	-0.061	-0.069	0.015	3.946	0.000
		Indirect effect	-0.004	-0.004	0.012	0.345	0.731
		Total effect	-0.057	0.064	0.066	0.862	0.389
	Food insecurity	Direct effect	-0.031	-0.032	0.009	3.396	0.000
Varity management strategies	Climate migration	Direct effect	-0.287	-0.276	0.068	4.241	0.000
		Indirect effect	-0.008	-0.009	0.012	0.668	0.505
		Total effect	-0.295	0.267	0.070	3.948	0.000
	Food insecurity	Direct effect	-0.062	-0.064	0.085	0.725	0.469
Food insecurity	Climate migration	Direct effect	0.134	0.131	0.059	2.282	0.023

Factor	Climate migration	Economic strategies	Food insecurity	Irrigation management strategies	Organic- oriented strategies	Sustainable development- oriented strategies	Crop variety management strategies
Climate migration	0.893
Economic strategies	-0.090	0.915
Food insecurity	0.237	-0.119	0.722
Irrigation management strategies	-0.465	0.248	-0.238	0.753
Organic-oriented strategies	-0.330	0.180	-0.244	0.503	0.795
Sustainable development -oriented strategies	0.316	-0.429	0.144	-0.560	-0.386	0.711
Crop variety management strategies	0.478	-0.122	0.136	-0.643	-0.457	0.432	0.774