Climate Change Vulnerability, Adaptation, and Feedback Hypothesis: A Comparison of Lower-Middle, Upper-Middle, and High-Income Countries

Abstract

Governments and policymakers are increasingly concerned about climate change. To cope with this inevitable issue, the SDGs-13 target underscores the importance of developing adaptation measures that reduce its adverse effects and ultimately safeguard both society and the environment. This issue is critical in developing countries, which are unable to counter climate-related risks because they lack adaptive capacity, suitable infrastructure, technology and, most importantly, human and physical capital. By contrast, resource-endowed developed countries have succeeded in integrating adaptative and protective policies into their developmental agenda using human power, technology, and especially investment. Keeping these facts in mind, this study is framed to examine the nexus between climate change, adaptation measures, and economic development across different income groups (lower-middle, upper-middle, and high income), using the Driscoll–Kraay (D/K) standard errors method for panel data from the period of 1995 to 2020. This study incorporates two indices (i.e., adaptive capacity and adaptation readiness) in the adaptation framework. The results demonstrate that developed countries such as Australia, Austria, Belgium, Canada, Denmark, France, Germany, Ireland, New Zealand, Sweden, Switzerland, the USA, and the UK are highly adaptive countries due to their readiness for adaptation. Developing countries with very low levels of readiness have a lower adaptive capacity and are, therefore, more vulnerable to climate change. Additionally, a non-causality test demonstrates that a one-way causality runs from readiness, ecological footprint, GDP, renewable energy, FDI, and natural resource investment to the adaptive capacity in all panels. The developed countries are less vulnerable to climate change because of their well-established economies, rich capital resources, good governance, and timely and effective readiness strategies. Adaptation readiness is a vital tool in capacity building for societal adaptation to minimize the effects of disasters on the living standard of communities.

1. Introduction

It is globally acknowledged that human-induced climate change is intensifying, affecting all nation-states in the arena. Adopting mitigation and adaptation measures is required to reduce these adverse effects [1]. Global warming will further increase the frequency of extreme weather events [2]. Therefore, the risk of these events (hydroclimatic incidents—heat waves or dry spells and erratic rainfalls or floods) leads to several consequences (i.e., ecosystem alternations, destruction of food crops, harm to public health, water system disruption, destruction to settlements, and infrastructure) [3].

Over the past two decades, the world experienced more than 11,000 extreme weather events, which were responsible for 475,000 human deaths, 94.9 million affected people, and USD 2.56 trillion in economic loss [4]. Scientific evidence demonstrates that a globally changing climate has a more vulnerable impact on poor countries and poor people than developed countries [5,6]. Unfortunately, developing nations are more susceptible to extreme weather events due to poor environments, poverty, and weak socio-economic status [7]. This issue is critical in developing countries, which are unable to counter climate-related risks because they lack adaptive capacity, suitable infrastructure, technology and, most importantly, human and physical capital when compared to developed countries with more certain assets [8,9]. Thus, these hazards are responsible for severe consequences across the globe due to a lack of adaptive capacity and readiness to combat climatic events [10].

The general scientific point is that climate change will increase the magnitude and frequency of climate variability––trends in moderate temperature, a driven-up sea level, a decreased snow cover, an increased upper-ocean warmth content, and extreme weather events provide regular signs of a warming planet [11]. These extreme events destroyed the stock of natural capital and livelihoods and created problems for sustainable economic development [12]. Therefore, helping countries enhance their capacity to adapt to changing climatic conditions is perceived as a development primacy [13].

Adaptation is an essential element in the impact assessment of vulnerability. It calls for capacity building for climate change mitigation. According to the Intergovernmental Panel on Climate Change, adaptive capacity is the intensity at which the human system, environment, and sustainable development occur or are “at-risk”. It is governed by climate change exposure and the power of the damaged system to adapt. The enhancement of adaptive capacity represents a practical means of coping with climatic extremes and uncertainties [14]. In addition, adaptation and mitigation strategies are effective policy response options. There is a need to improve and assess the capacity of the system to adapt. Thus, it will be helpful to deal with the system’s susceptibility to climate change [15]. Strategies to adapt to climate change entail taking accurate actions to decrease the negative climatic stimuli or exploit the beneficial opportunities by defining suitable adjustments and changes in human and natural systems [16,17].

Adaptation strategies have three potential objectives, which are (a) to lessen the exposure to the risk of hazard, (b) to increase the adaptive capacity to deal with inevitable damages, and (c) to take advantage of readiness opportunities [18]. Adaptation strategies rely heavily on the concept of adaptive capacity (adaptability) that appears in the human environment of climate change. It captures the symbol of an affected system’s ability to modify in response to the sources of stress in their ambiences [19] to reduce risk exposure and enable adaptation [20,21]. Thus, potential vulnerability to climate change is due to exposure and sensitivity related to climatic extremes, whereas adaptive capacity represents moderate sources of potential impacts [22,23].

The adaptive capacity of populations and economies is widely acknowledged to be a critical factor in adaptation [19,20,21,22,23,24], which is determined by their socio-economic characteristics and may vary mainly over time amongst socio-economic groups and countries [25,26]. The most vulnerable groups, regions, and countries are the ones that have a limited coping capacity to handle high exposure to the harmful effects of climate change [27]. The literature suggests that increasing adaptive capacity is a practical way to counter the risks posed by climate change. The adaptation method’s main objective is to design communities that are resilient to climate change. To avoid the vulnerabilities related to climate variability, adaptation readiness (preparedness) appears as a strategic element that can mitigate adverse climate shocks [24]. Adaptation readiness assesses the extent to which key socio-economic and governance factors are considered fundamental in determining how and when adaptability occurs [28].

Therefore, a country’s adaptive capacity level can serve as a proxy for tracking adaptation. It is worth mentioning that adaptive capacity differs from adaptation readiness: the readiness to adapt studies what has been done to lay the ground for adaptation, while the former focuses on conditions that determine adaptability [29]. In other words, adaptation readiness is the framework for supporting adaptive capacity, i.e., the context-specific problems that guarantee the shift from potential to action [6]. As such, the goal of adaptation readiness is to provide an indication of how likely it is that adaptation will occur and identify potential areas in which actions can enhance preparedness [28,29,30], while adaptive capacity is the ability of a system to develop in response to policy changes or to absorb environmental risks and broaden the range of variability with which it can cope [27].

Adaptive capacity has been discussed and measured in past studies [19,27,31,32,33]. However, it is hardly seen in the studies on the impact assessment of vulnerability to climate change and adaptation. This study considers that a system must “be able to adapt” before adaptation can be made. Furthermore, adaptive capacity is greatly affected by both access to resources and the cost of using these resources [34,35]. Although adaptation strategies have been developing at different national (indigenous) and universal levels regarding adaptation implementation, there are still insufficient shreds of evidence.

Consequently, a move has begun to re-evaluate adaptation such as capacity building [35]. Climate change is a problem that necessitates assessing its impacts on the economy and developing adaptation strategies accordingly; however, studies on adaptive capacity and adaptation readiness are limited [10]. Therefore, there is an enormous need to evaluate the adaptive capacities of communities or even whole countries as an excellent way to adapt to extreme climatic changes.

2. Literature Review

The climate change adaptation investigation was initiated by Smit and Wandel [24], with adaptation strategies emerging as a vital component of climate policy [36,37,38,39]. Climate change has detrimental effects on all life-supporting sectors and human well-being worldwide. Due to the lower capacity to adapt [10], limited literature on enhancing adaptation capacity is available. It may produce effects beyond the level of climate change coping capacity on adaptation [37]. The extent to which climate phenomena affect our society depends on exposure, adaptive capacity, the type of hazards, and socio-economic development. However, coping capacity is a form of the future sustainability of society that is needed at the local, regional, national, and international scales [38].

Some studies focused on measuring the adaptive capacity of cities to understand the potential for adaptation [26,40]. These studies developed a conceptual framework to assess urban adaptive capacity by focusing on three key dimensions, such as (a) characterization, (b) external factors, and (c) and the dynamics of adaptive capacity. Some studies [41,42] categorized stumbling blocks to adaptation (e.g., financial, governance, social institutions, and geographical factors that constrain adaptation), while some studies [43] pointed out gaps between existing and potential adaptation.

However, most studies on climate change showed concern for the Asian and African continents, which merely modeled the biophysical relationship between crops and their surrounding environment [44,45,46]. Instead, studies need to consider factors that are likely to influence the adaptive capacity of farmers, which is essential to effectively target adaptation and capacity initiatives in the agricultural sector [47]. The adaptability of the agriculture sector to climate change may be an essential aspect of human survival and food security [48].

Furthermore, climate risks were responsible for the shift in seasonal patterns, changing temperatures, precipitation variability, ocean-related effects, higher mortality and morbidity, desertification, and coastal and soil deterioration, contributing to vulnerability in various countries [49,50]. It is necessary to understand the interrelated nature of such sectors and the associated coping capacity. Some argue that capturing the extent to which human civilizations are prepared to adapt is determined by the adaptive readiness to deliver an indication or degree of the probability of adaptation taking place [28,51,52]. In contrast, others used adaptation readiness attributes composed of economic, social, and governance indicators [52,53].

Thus, it is hypothesized that adaptation requires information and knowledge in the form of early warning signs and adaptation preparedness to combat climatic variability, as is shown in

Table 1. Selected literature regarding adaptive capacity and climate change.

Authors	Variables	Methods	Countries	Duration	Results
[10]	Vulnerability and readiness indicators	Pooled OLS, PMG, panel quantile regression	192 countries	1995–2016	(1) Africa was the most vulnerable region to climate change, with a high exposure and sensitivity and a low adaptive capacity. (2) Developed countries were less vulnerable due to higher adaptive capacity.
[55]	Crop yield, infrastructure, temperature, drought, flood exposure, agricultural capacity, and readiness	OLS and Fixed effect	167 countries	1995–2010	(1) A high adaptive capacity makes the agricultural systems less vulnerable to climatic risks.
[56]	Exposure, sensitivity, and adaptive capacity indicators	DEA	141 UN countries	1995–2016	(1) Least-developed countries often lack the adaptive capacity and are more vulnerable to climate change.
[57]	Vulnerability, perception, and readiness indicators	Cluster analysis	17 Latin American countries	1995–2017	(1) There is an inverse relationship between climate change acknowledgment and the perception of the market economy.
[58]	Temperature, precipitation, adaptive capacity, and readiness	Mapping and normalization	5 African countries	1991–2016	(1) Northern and Southern Africa were the most ready for climate change adaptation. (2) West Africa has a lower adaptive capacity and is the least ready, while East and Middle Africa were in the middle.
[59]	Vulnerability indicators	Cluster analysis	MENA countries	1995–2017	(1) MENA countries have a lower adaptive capacity, with poor water and food conditions.
[60]	GDP, FDI, institutional quality, capital efficiency, savings, trade openness, temperature, precipitation, adaptation readiness, and latitude	GMM	44 African countries	2006–2016	(1) Increased temperature has an adverse impact on productivity and economic growth.
[6]	Vulnerability and readiness indicators	Panel quantile regression	51 African countries	1995–2018	(1) Adaptation readiness has a substantial negative effect on climate change vulnerability.
[50]	Vulnerability and readiness indicators, income level, and GHG emissions	Romano–Wolf correction, 2SLS	192 countries	1995–2017	(1) High-income countries with high adaptive capacity and readiness have lower climate susceptibility. (2) Low-income countries are more vulnerable to climate change.

. However, the literature focuses on the immediate drivers and damages of extreme weather events, failing to account for the level of adaptive capacity to mitigate the extent of climate vulnerabilities. Thus, this study contributes to the global climate change mitigation and adaptation debate by (a) providing a sectoral assessment of adaptive capacity across the food, water, health, ecosystem, infrastructure, and human habitat sectors, (b) investigating the adaptation readiness to combat climate change and its impact on adaptive capacity, (c) assessing climate change vulnerability using the ecological footprint, (d) incorporating investment in natural capital, FDI inflow, and renewable energy into an adaptation framework, and (e) analyzing the causal association among enlisted variables using the Juodis et al. [54] Granger non-causality approach, underscores the incredible contributions of this study.

3. Theory and Model

3.1. Theoretical Framework

From an economic perspective, the relationship between climate risks and exposure levels can be formulated regarding mitigation and adaptation [1]. Indeed, hazard mitigation today requires a strong “adaptive capacity and readiness” that will likely increase “resilience” in the future [13,35,61]. Today, the world experiences vulnerability to climate change in extreme weather events, and the variability caused by human activities has wide-ranging, non-uniform effects (Figure 1). Thus, accumulating facts indicate that responses to climate change vary with vulnerability due to dissimilarity in exposure and sensitivity to climatic shocks as well as the adaptive capacity of a system to cope with unusual situations [50,62,63,64]. Despite international commitments to encourage climate change adaptation, current and future developments are constantly being planned regardless of any concerns about the biophysical limits to growth [65].

U’Thant first presented the rudiments of the limits to growth approach in 1969. The prospects of limited natural resources and the occurrence of a global climate catastrophe were printed in an article entitled “The Limits to Growth” [66]. The problem of non-sustainable development is still relevant, as highlighted in The Club of Rome’s recent publication, which pointed out that all existing socio-ecological problems could be related to the consequences of endless growth on a finite planet [65]. One of the solutions to this problem is adaptive capacity, which represents a concept and a tool for sustainable development for human settlement [67].

The climate change dilemma was brought to light at an international summit by the Intergovernmental Panel on Climate Change (IPCC)’s first Assessment Report in 1990. This report highlighted the drivers behind the change, the pattern of the climate system, and the effects of climate change on socio-economic and ecological systems [68]. The results of the first IPCC assessments contributed to the organization of the United Nations Framework Convention on Climate Change (UNFCCC) (1992/1994). Following the 1992 Rio Earth Summit, the UNFCCC aims to stabilize atmospheric greenhouse gas concentrations within a time edge that allows ecosystems to adapt naturally and enables sustainable development. UNFCCC became active in 1994 and set the framework for vital agreements, such as the Kyoto Protocol (1997/2005) and the Paris Agreement (2015/2016). After the failure of the Kyoto Protocol to produce significant reductions in global emissions, the Paris Agreement was eventually adopted at the COP21 to limit global temperature increase [17,35,69]. In 2016, the Paris Agreement set a global adaptation goal of “strengthening adaptive capacity, improving resilience, and lessening vulnerability to climate change to contribute to sustainable development.” Thus, the implementation of the Paris Agreement, and the UNSDGs specifically (SDGs-13), helps to guide global efforts toward actions that will reduce greenhouse gas emissions while building climate resilience [1]. The Agreement strongly emphasizes climate-related capacity-building for underdeveloped countries and calls on all developed countries to support capacity-building measures in developing countries according to the standards (ISO 14090:2019 and ISO 14091:2021) of adaptation to climate change [2,5,13].

3.2. Model Specification and Data

This study empirically analyzed the adaptation readiness, climate change, and economic development nexus across different income groups (i.e., 40 lower-middle, 30 upper-middle, and 45 higher-income countries;

Table A1. 40 lower-middle, 30 upper-middle, and 45 higher-income countries.

Income Group	Name of Countries
Lower Middle-Income Countries (LMIC)	Algeria, Angola, Bangladesh, Bolivia, Belize, Bhutan, Cape Verde, Cote d’Ivoire, Dominica, Egypt, Ecuador, El Salvador, Ghana, Guatemala, Honduras, India, Indonesia, Iran, Jamaica, Jordan, Kyrgyzstan, Moldova, Mongolia, Mauritania, Morocco, Myanmar, Nicaragua, Namibia, Pakistan, Paraguay, Peru, Philippines, Samoa, South Africa, Sri Lanka, Tonga, Tunisia, Ukraine, Uzbekistan, Viet Nam.
Upper Middle-Income Countries (UMIC)	Albania, Argentina, Armenia, Azerbaijan, Bulgaria, Belarus, Bosnia & Herzegovina, Botswana, Brazil, China, Colombia, Costa Rica, Dominican Republic, Fiji, Gabon, Georgia, Grenada, Guyana, Kazakhstan, Lebanon, Libya, Malaysia, Mauritius, Mexico, Panama, Russian Federation, Saint Luci, Suriname, Thailand, Turkey.
High-Income Countries (HIC)	Australia, Austria, Bahamas, Barbados, Belgium, Brunei Darussalam, Canada, Chile, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Japan, Korea Rep., Kuwait, Latvia, Lithuania, Luxembourg, Malta, Netherlands, New Zealand, Norway, Oman, Poland, Portugal, Saudi Arabia, Singapore, Slovenia, Spain, Sweden, Switzerland, Trinidad and Tobago, United Arab Emirates, United Kingdom, United States of America, Uruguay.

). Based on the data availability, the study used panel data from 115 countries from 1995 to 2020. Adaptive capacity is a system’s ability or potential to respond to climate stimuli [35]. Adaptive capacity data was taken from the Notre Dame Global Adaptation Initiatives (ND-GAIN) index [70]. Adaptive capacity covers the adaptive capacity of six life-supporting sectors (food, water, health, human habitat, ecosystem services, and infrastructure) based on specific indicators such as (a) agricultural capacity, (b) child malnutrition, (c) access to safe and clean water supplies, (d) dam capacity, (e) access to clean and better-quality sanitation conditions, (f) better medical staff (g) biomes protected, (h) engagement in environmental treaties, (i) trade quality and infrastructure capacity, (j) paved transportation structure, (k) access to electricity, and (l) disaster readiness [53]. The world map shows the country-wise average of the adaptive capacity index (Figure 2).

The following model shows the link of dependent variable adaptive capacity with explanatory variables:

ADC_it = f (RED_it, EF_it, GDP_it, FDI_it, REN_it, NC_it)

(1)

where ADC represents an adaptive capacity index (0–100) and RED represents an adaptation readiness index (0–100) that defines adaptation readiness as preparedness to make effective use of investments for adaptation. It has three components (a) social readiness (social inequality, education, ICT, and innovation), (b) economic readiness, including easiness of doing business, and (c) governance readiness (political stability, the rule of law, control of corruption and regulatory quality) [53]. The world map shows the country-wise average of the adaptation readiness index (Figure 3).

EF represents total ecological footprints per capita (gha) and is used as a proxy for climate change [71]; GDP represents the GDP per capita (constant; USD 2015), which is used as a proxy for economic development; FDI represents the foreign direct investment and net inflows (% of GDP); REN represents the renewable energy use (% of total energy); the data regarding these variables are taken from the World Bank’s Development Indicators (WDI) [72]. NC represents the investment in natural capital (% of total protected areas); and i and t subscripts denote countries and time, respectively. Equation (1) is converted into a double-log model to control for outliers and the heteroscedasticity effect. It expresses the elasticity coefficient between regressed and explanatory variables [73]:

$$\begin{gathered} \mathrm{l}\mathrm{n}{\mathrm{A}\mathrm{D}\mathrm{C}}_{\mathrm{i}\mathrm{t}}={\mathrm{\beta }}_0+{\mathrm{\beta }}_1{\mathrm{l}\mathrm{n}\mathrm{R}\mathrm{E}\mathrm{D}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\beta }}_2{\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{F}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\beta }}_3{\mathrm{l}\mathrm{n}\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\beta }}_4{\mathrm{l}\mathrm{n}\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{i}\mathrm{t}} \\ +{\mathrm{\beta }}_5{\mathrm{l}\mathrm{n}\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\beta }}_6{\mathrm{l}\mathrm{n}\mathrm{N}\mathrm{C}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}} \end{gathered}$$

(2)

where β₀ is the constant term, the symbols β₁ to β₆ appear as coefficients of explanatory variables, and the error term is given by ε.

3.3. Justification of Explanatory Variables

3.3.1. Adaptation Readiness

Adaptation readiness is proposed as a complementary concept to adaptive capacity and is a fundamental pillar of adaptation strategies which captures the strength and existence of governance structures [6,58]. Adaptation readiness (preparedness) is described as “a measure of how individual values (like nations, businesses, regions, societies, and so on) are willing to adapt, to characterize whether human systems are ready and prepared to do adaptation” [29]. The concept of “readiness” provides a defining framework in which countries are about to make and achieve their adaptation goals to mitigate the effects of climate change [28].

Adaptation readiness, particularly the state’s capability to influence investments and transform them into adaptation measures, determines the scope to control the impacts of disruptive trends on economic, social, and governance structures. By concentrating on what is being done to plan and prepare for adaptation, adaptation readiness has the potential to enhance adaptive capacity and alleviate the adverse impacts of climate change [43,50].

3.3.2. Ecological Footprint

The ecological footprint is an estimate of the bio-productive (land and water) area essential to support human consumption compared with the available bio-capacity, which is the potential to supply ecosystem resources and waste assimilation [74,75]. Many countries have bio-capacities that are declining quickly while ecological footprints are rising. This may be a combined force of higher population growth, rapid CO₂ emission, and deforestation. Therefore, the ecological footprint measures climate change comprehensively beyond measuring CO₂ as it calculates the built-up land, carbon footprint, cropland, grazing land, fishing grounds, and forest resources [71,75], thus taking into account all aspects of ecological dynamics. The adaptive capacity of ecosystems is increasingly weakened with industrial development and climate change. Therefore, adapting to new changes in the ecological environment by enhancing the level of adaptive capacity and increasing resilience is required [76]. The carbon footprint (the total amount of greenhouse gas emissions) is one of the most significant drivers of the overall ecological footprint that significantly impacts climate change. This infers that the ecological footprint is highly involved and representative of assessing climate stimuli [77].

Moreover, the latest climate report from the IPCC warned that mounting CO₂ may possibly soon surpass the ability of many countries to adapt [13]. Economic development usually means consuming more resources and generating carbon emissions. Developed and emerging countries are at the forefront of CO₂ emissions per capita when compared to developing nations. Only household consumption is responsible for 65% of the CO₂ emissions and between 50% and 80% of the total material, land, and water usage [78]. Similar to emissions levels, the footprint size is larger, and the heavier strain on the environment seems to correlate negatively with adaptive capacity.

3.3.3. Economic Growth

Low-income countries are considered more vulnerable to climate variability when compared to rich countries [79,80]. This insight is built on an empirical observation that investigated the impact of past extreme climate events [81,82,83]. Lower- and upper-middle-income countries have less ability to deal with climate stress because they have an insufficient economic, institutional, and financial capacity to adapt more efficiently than developed countries [8]. Some studies highlight the greater exposure of low-income nations because of semiarid climates or the overcrowding of populations. Others point out the high sensitivity to climate hazards in low-income countries is due to a dependence on climate-sensitive sectors (i.e., agriculture); these two factors matter [80,84]. However, the most vigorous justification is the existence of a low adaptive capacity and the poor implementation of adaptation strategies in these countries [24,85]. These factors have contributed significantly to economic development via economic growth.

The developed countries are less vulnerable to climate change because of their well-established economies, rich capital resources, good governance, and timely and effective readiness strategies [10]. The agricultural sector is one of the ultimate areas responsible for 30 to 40% of all greenhouse emissions; thus, the agricultural sector is substantially impacted by heatwaves and precipitation extremes [86,87]. Climatic extremes will primarily impede economic development through lost productivity, destruction of infrastructure and property, mass migration, and injury to human habitat and health. Overall, the aggregate result of economic development will most likely be negative in the long run [1].

3.3.4. FDI Inflow

Foreign direct investment (FDI) has been a critical driver of economic growth, a source of employment, and the transfer of advanced technologies to host countries [88,89]. Regarding climate change, studies have primarily focused on the relationship between FDI and carbon emissions [90,91]. The FDI inflow to poor countries helps transfer management techniques and technologies that reduce carbon emissions. The influence of FDI could also promote industrial competitiveness and environmental quality in developing nations [92,93]. Physical climate risks increasingly impact industries’ ability to adapt globally, notably through foreign direct investment (FDI). For instance, increased precipitation and floods hampered Toyota’s manufacturing operations in Southeast Asia [94].

Moreover, risks from sea levels uphill affected Chinese infrastructure investments in Pakistan [95]. Foreign direct investment (FDI) has a role in limiting climate change by supporting projects that decrease CO₂ emissions and green projects that promote sustainable development [96]. For instance, in China, foreign-owned businesses reduced their emissions more quickly than domestic ones. According to reports, Chinese businesses can now quickly upgrade emission-intensity-related technologies due to the FDI inflow [97]. FDI is expected to positively affect economic growth and the environment through the increase in total investment and production efficiency [98].

3.3.5. Renewable Energy

The energy system is a primary human need and the spine of any economy. According to a report, high risks manifested globally through increased emissions of greenhouse gases, surges in energy prices, and, unfortunately, adapting to climate change [99]. Likewise, to address these stresses, communities have an inadequate capacity which undermines the development and stability of economies and their localities [100]. In order to cut emissions of greenhouse gases and combat climate change, there is now more focus on using clean and renewable energy sources [98,101]. The availability of clean, reliable, and affordable modern energy is supported by the Sustainable Development Goals (SDGs-7). Renewable energy technologies (RETs) have been presented as a rampant means of adapting to climate change while reducing advanced greenhouse gas emissions and boosting carbon sequestration in the least-developed and developed countries [102]. The transition from conventional to renewable energy from resources, i.e., solar, tidal, wind, biogas, hydro, and geothermal [103], offers an opportunity to adapt while simultaneously enhancing adaptive capacity [104]. Renewable energy reduces resource dependency and CO₂ emissions in the atmosphere and helps increase the standard of living [105].

3.3.6. Natural Capital Investment

Extreme weather events accelerate the depletion of natural capital and the ecosystem services it provides, as evidenced by significant losses in forests, arable land, water resources, wetlands, mangroves, and wide varieties of fauna and flora that have even become extinct [106]. Today’s enormously resource-intensive development impedes the Earth’s limited capacity to surpass human well-being. Society needs to invest in restoring this capacity and adapt to it, honoring the legitimate aspirations of people and poorer nations for improved living standards, according to the report “Making Peace with Nature” [107]. Investments in natural capital imply unique opportunities for substantial socio-economic and environmental benefits, with increased adaptive capacity having a greater ability to adapt and transform in the face of change [67]. For instance, investment in natural capital, such as spending USD one million annually on forest management, can create between 500 and 1000 jobs in various developing countries [108]. Moreover, investments in ecosystem conservation yield capacity-building gains that are worth four times more than the original investment. In particular, nations prone to floods, such as India, China, Bangladesh, Pakistan, Vietnam, and Indonesia, and nations prone to forest fires, such as Brazil, Australia, and the USA, should leverage spending to invest in natural capital and resilience building [109,110]. These investments also protect nature-reliant industries, such as tourism, agriculture, and water management [111].

4. Econometric Methods

The empirical analysis involved different tests, which were categorized as (a) a descriptive analysis, (b) pre-estimation diagnostic tests, (c) unit root assessments, (d) a cointegration analysis, (e) a regression estimation, and (f) a causality analysis.

4.1. Pre-Estimation Diagnostic Tests

In panel data analysis with models for which the number of cross-sectional units is large relative to the number of time periods (N > T), the error term (${\epsilon }_{i,t}$) may experience econometric issues such as cross-sectional dependence, autocorrelation, and heteroscedasticity in the panels. It is better to check several econometric issues before regression estimation to obtain better results. Therefore, this study used several diagnostic tests, such as cross-sectional dependence, slope heterogeneity, and heteroscedasticity tests.

4.1.1. Cross-Sectional Dependency (CD)

Initially, in the panel data estimation, there was a need to confirm whether the variables under consideration were cross-sectionally independent or dependent. This was checked using the cross-sectional dependence (CD) test, which eliminates the average in the correlation computation [112,113]. The problem of CD exists with cross-country linkages (i.e., a shock in any economy transfers to other economies) due to rapid globalization, economic, regional, and financial integration, and the presence of externalities [114]. Therefore, multiple CD tests such as the (a) Breusch and Pagan LM test, (b) Pesaran bias-adjusted LM test, and (c) Pesaran CD test [115,116,117] were used in this study.

4.1.2. Slope Heterogeneity Test

Under the null hypothesis (i.e., Ho: βi = β for all i & H₁: βi ≠ βj for i ≠ j) and alternative hypothesis, checking the slope heterogeneity is a prerequisite. When the number of time periods (T) is greater than the number of cross-sectional units (N), a slope homogeneity test, which can be suitable for the existence of heteroskedasticity, is presented [118]. For large samples, when N, T→∞, a modified form of Swamy’s test, the $\hat{\Delta }$, is used. On the other hand, when samples are small, Swamy’s ${\hat{\Delta }}_{\mathrm{adj}}$ test is applicable.

4.1.3. Panel Heteroscedasticity Test

One of the assumptions in a linear regression analysis is that the error terms have constant variance. If this assumption is not met, the errors are said to suffer from the problem of heteroscedasticity. Heteroscedasticity is mainly due to outliers in the data. It leads to biased estimates of OLS [119]. A modified test statistic for heteroscedasticity in a linear regression model was provided by Cook and Weisberg [120] to examine the association between the residuals variance and the explanatory variables. This test computes an auxiliary regression model as:

$$\widehat{u}2i={\alpha }_1+{\alpha }_2{Z}_{3i}+\dots +{\alpha }_p{Z}_{pi}+vi$$

(3)

The null hypothesis shows homoskedasticity; the test statistic distribution is asymptotically chi-square (ꭓ²), with q degrees of freedom varying across cross-sections; and the test is right-tailed [121].

4.1.4. CIPS Unit Root Test

After the CD analysis, the next stage of the econometric process was to check for the correct order of integration. A stationary time series was required, one whose statistical properties were time-independent as using non-stationary variables may lead to meaningless forecasts and spurious results [113]. As the presence of CD among cross-sections, to tackle this problem, it was appropriate to use second-generation panel unit root tests. An augmented cross-sectional IPS (CIPS) [122] test statistic is described as follows:

$$CIPS=\frac{1}{N}\sum _{i=1}^N{\tilde{t}}_i$$

(4)

It tested the null hypothesis, whose rejection indicated stationarity in the underlying series.

4.1.5. Panel Cointegration Test of Westerlund

The CD indicated using a second-generation cointegration test such as the Westerlund test [123]. It was a dire requirement to confirm the long-run association among variables. The long-run connection suggested a cointegration between two or more variables [124]. This test is built on structural dynamics compared to residual dynamics; thus, the panel cointegration test statistics are estimated as follows:

$$VR=\sum _{i=1}^N\sum _{t=1}^T{\widehat{E}}_{it}^2{\widehat{R}}_i^{-1}$$

(5)

$$VR=\sum _{i=1}^N\sum _{t=1}^T{\widehat{E}}_{it}^2{\left(\sum _{i=1}^N{\widehat{R}}_i\right)}^{-1}$$

(6)

4.1.6. Long-Run Estimation Method

If the long-run cointegration exists among the variables with the short-run adjustment process, the OLS estimation results may provide a biased coefficient of parameters and misleading inferences. Moreover, the presence of heteroscedasticity can render standard errors of coefficients; therefore, their t-values are unlikely to be accurate, and the R² is inflated. Consequently, the coefficient estimates from common panel models, such as the fixed effect (FE) and random effect (RE), may provide biased coefficients of inconsistent and inefficient parameters. To deal with unobserved heterogeneity, linear dynamic models were used for the panel data [125]. This study employed the Driscoll and Kraay (D/K) standard errors approach presented by [126]. It is a non-parametric technique that estimates pooled OLS regression models to find coefficients in the panel data [126]. It accepts heteroskedasticity in the error structure, and correlation occurs between the panel’s cross-sectional units. It is well calibrated due to (a) the existence of cross-sectional dependence, (b) control over missing observations, (c) encountering an econometric problem of heteroscedasticity, and (d) possible application to both balanced and unbalanced panels [6,10,111].

This study applied the Driscoll and Kraay standard errors via a linear regression model. Primarily, the mean values were computed using the explanatory variables and errors. Second, to obtain standard residuals that are appropriate against the CD, these averages were used in weighted heteroscedasticity as [127]:

$$y_{i.t}={x\mathrm{ʹ}}_{i,t}\beta +{\epsilon }_{i,t}\hspace{1em}i=\mathrm{1,2},\dots \hspace{0.17em}\dots ,{\rm N},t=\mathrm{1,2},\dots \hspace{0.17em}\dots ,{\rm T}$$

(7)

where the dependent variable is $y_{i.t}$ and the independent variables are shown by ${xʹ}_{i,t}$ with a (K + 1) × 1 vector. Here, the first element is 1, and the unknown coefficients are $\beta ,$ with a (K + 1) × 1 vector, and subscripts $i \mathrm{a}\mathrm{n}\mathrm{d} t,$ which represent countries and time, respectively [10]. Moreover, the term ${\epsilon }_{i,t}$ may have econometric issues in the panel, such as CD and heteroscedasticity, so $\beta$ can be consistently estimated by using OLS regression [10,111].

4.1.7. Panel JKS Causality Test

The study adopted the recent version of the Granger non-causality, attributable to the JKS non-causality test [54], to explore the causal relationship between the predicted variable and several predictors. A Granger causal relationship exists between any two x and y variables, for example, when the current value of the y variable is forecasted using the x variable’s past values [128]. There are different methods of proceeding with the Granger non-causality test. The prominent methods are the generalized method of moments (GMM) offered by [129], which is applicable when time series observations (T) are small to homogeneous panels and when the T is moderately large. The method of [130] is valid for heterogeneous panels. However, this method is subject to size distortion when used with a small number of T observations [54]. Since the panel under consideration is neither sizeable nor homogenous, all these methods may cause estimation bias [131].

To overcome these problems, this study applied the JKS causality test. This test adapted the half-panel jackknife (HPJ) technique to derive unbiased estimators using the Wald test. By assuming that the data are stationary in the split-panel jackknife (SPJ) class, the HPJ estimation reduces the higher-order bias. This approach is unique as it allows for cross-sectional heteroskedasticity in the residuals. It provides a robustness appropriate for our dataset, which includes panel data with heterogeneous parameters, a moderate time dimension, and high persistence [131,132]. The JKS functional form can be presented as follows [133]:

$${Ŵ}_{HPJ}={\rm N}{\rm T}{\beta }^{ʹ}({Ĵ}^{-1}Ṽ{Ĵ}^{-1}{)}^{-1}{\beta }^{~}\to {ꭓ}^2(P)$$

(8)

5. Results and Discussion

5.1. Descriptive Analysis

Descriptive statistics of LMIC, UMIC, and HIC variables from 1995 to 2020 are shown in

Table 2. Descriptive Statistics.

Panel	Mean	Standard Deviation	Minimum	Maximum	Source
Adaptive Capacity Index (ADP) (0–100)
LMIC	3.38512	0.3353695	2.578799	4.121953	ND-GAIN
UMIC	3.855599	0.1904721	3.451918	4.274185	[70]
HIC	4.050107	0.1856814	3.49365	4.365746
Foreign Direct Investment (FDI, NI) (%GDP)
LMIC	1.6728079	1.258294	−12.94238	3.78219	WDI [72]
UMIC	1.114444	1.201322	−6.25558	4.008611
HIC	0.9107528	1.59848	−7.198535	6.107207
Gross Domestic Product (GDP) (2015 US$)
LMIC	7.831628	0.6047347	5.420899	8.976406	WDI [72]
UMIC	8.669545	0.5186003	6.644984	9.620661
HIC	10.20977	0.619819	8.504314	11.62998
Renewable Energy Utilization (RE) (% of total final energy use)
LMIC	2.903098	1.404749	−2.830218	4.547242	WDI [72]
UMIC	2.628965	0.9952329	0.1428875	4.516352
HIC	1.447315	2.484049	−7.600903	4.137216
Ecological Footprint (EF) (Global hectares per capita)
LMIC	0.634414	0.490187	−0.7131985	2.1095	GFN [71]
UMIC	0.9825846	0.3395404	0.0090686	1.91447
HIC	1.720024	0.3316092	−0.0822707	2.875039
Adaptation Readiness Index (RED) (0–100)
LMIC	3.540728	0.1933363	2.858467	4.122351	ND-GAIN
UNIC	3.681334	0.1715679	3.301396	4.129827	[70]
HIC	4.023328	0.2023284	3.396999	4.402329
Natural Capital Investment (NC) (% of total protected areas)
LMIC	3.001522	0.8034843	0.7965264	5.135757	ND-GAIN
UMIC	2.400941	1.449011	−4.64232	4.885057	[70]
HIC	0.8138209	1.624007	−7.374979	4.833475	IUCN [109]

. Developed countries have a more remarkable ability to deal with climate risks (i.e., HICs and UMICs). In contrast, developing countries have limited-capability LMICs (3.38512), even though they become more vulnerable to climate change. Adaptation readiness is needed to adapt to the impacts of climate change and aims to help strengthen countries’ adaptive capacity. It shows that the adaptation readiness (economic, social, and governess readiness) was higher in HICs (0.425). At the same time, the value of the readiness index was less in UMICs (0.162) and LMICs (0.129). The total ecological footprint per capita was higher in HIC s(1.720 gha), followed by UMICs (0.982 gha) and LMICs (0.634 gha). This illustrates that the total ecological footprint is directly associated with the level of productivity or income.

The role of FDI is also essential in transferring green technology from developed countries to developing countries. The average FDI inflow was greater in LMICs and UMICs (1.672 and 1.114% of GDP) and less in HICs (0.910% of GDP). On the other hand, natural capital investment was greater in LMICs (3.001%) and less in HICs (0.813%). This demonstrates that LMICs are more concerned about the conservation of natural resources. Renewable energy use is vital for conserving conventional energy resources and environmental protection to increase adaptive capacity. The share of renewable energy in the total energy mix was higher in LMICs (2.903%), followed by UMICs (2.628%) and HICs (1.447%). The share of GDP per capita was more in HICs (10.209%), followed by UMICs (8.669%), and less in LMICs (7.831%). The trend analysis of the mean of selected variables in three panels is presented in Figure 4. Appendix A shows a list of selected countries.

5.2. Diagnostic Tests Results

In a panel data analysis, some econometric problems such as CD, slope heterogeneity, multicollinearity, and heteroscedasticity are possible. Therefore, different diagnostic tests were performed (

Table 3. Diagnostic test results.

Problem	Test	Lower-Middle Income		Upper-Middle Income		High Income
		Test.stat.	Prob.	Test.stat.	Prob.	Test.stat.	Prob.
Cross S.D	Breusch–Pagan LM	1089 ***	0.000	657.3 ***	0.000	1327 ***	0.000
	Pesaran LM adj	10.36 ***	0.000	11.79 ***	0.000	8.9 ***	0.000
	Pesaran CD	1.809 **	0.071	8.732 ***	0.000	5.094 ***	0.000
Slope Heterogeneity	∆	24.406 ***	0.000	17.322 ***	0.000	21.966 ***	0.000
	${\hat{\Delta }}_{\mathrm{adj}}$	29.333 ***	0.000	20.818 ***	0.000	26.400 ***	0.000
Heteroscedasticity	Breusch–Pagan /Cook–Weisberg	33.23 ***	0.000	8.59 ***	0.000	3.66 ***	0.003

** Significant at 5% and *** Significant at 1%.

). They demonstrated the results of several CDs because of the presence of CD in three panels. It was appropriate to apply a second generation test due to the existence of CD. Slope homogeneity tests, both the delta ($\hat{\Delta }$) test and adjusted delta ($\hat{\Delta }$) test, verified slope heterogeneity in every panel. According to the Breusch–Pagan/Cook–Weisberg test, heteroscedasticity existed in all the panels.

5.3. Panel Unit Root Test Results

Table 4. CIPS test results.

Variables	Lower-Middle Income	Upper-Middle Income	High Income
At level (Intercept and Trend)
lnADC	−2.399	−2.761 **	−2.534
lnFDI	−3.579 ***	−3.782 ***	−3.870 ***
lnGDP	−1.667	−2.184	−2.253
lnRED	−2.026	−1.460	−2.164
lnEF	−3.036 ***	−3.320 ***	−3.210 ***
lnRE	−2.053	−2.317	−2.676 **
lnNC	−3.623 ***	−3.150 ***	−4.165 ***
At first difference (only with intercept)
lnADC	−4.322 ***	−4.621 ***	−4.787 ***
lnFDI	−5.282 ***	−5.550 ***	−5.726 ***
lnGDP	−3.180 ***	−3.655 ***	−3.373 ***
lnRED	−4.199 ***	−3.788 ***	−4.504 ***
lnEF	−5.096 ***	−5.204 ***	−5.252 ***
lnRE	−4.687 ***	−4.524 ***	−4.797 ***
lnNC	−2.565 ***	−3.102 ***	−2.697 ***

** Significant at 5% and *** Significant at 1%.

displays the CIPS unit root test results. This test was applicable in cases such as at level with intercept and trend and with a first difference. In the first case, i.e., at the level and with intercept, only three variables (ecological footprint, FDI, and natural capital investment) were stationary in all panels. However, in the second case, all selected variables showed stationarity at a 1% significance level at the first difference. The stationarity is necessary for variables, implying that the regression estimates are reliable.

5.4. Cointegration Test Results

Due to the existence of CD in all panels, the Westerlund cointegration method was applied [123] to confirm a long-run association between the selected indicators.

Table 5. Cointegration test result.

Panel	Variance Ratio
	Statis.	Prob.
LMIC	−1.5102 ***	0.0006
UMIC	2.7331 ***	0.0031
HIC	−2.3670 ***	0.0090

*** Significant at 1%.

shows cointegration test results, and the significant test statistic confirmed a long-run relationship between concerning variables. It was required to elude spurious correlations.

5.5. Regression Analysis

Only long-run coefficients are presented in

Table 6. Regression estimation.

Variables	Lower-Middle			Upper-Middle			High Income
	Coff.	Std. Err.	Prob.	Coff.	Std. Err.	Prob.	Coff.	Std. Err.	Prob.
lnFDI	−0.008	0.008	0.336	0.008	0.010	0.380	0.022 ***	0.006	0.003
lnGDP	−0.200 ***	0.015	0.000	0.292 ***	0.027	0.000	−0.043 **	0.019	0.038
lnRED	0.375 ***	0.038	0.000	0.575 ***	0.075	0.000	1.070 ***	0.053	0.000
lnEF	−0.178 ***	0.009	0.000	−0.434 ***	0.028	0.000	−0.238	0.037	0.105
lnRE	0.046 ***	0.002	0.000	0.081 ***	0.008	0.000	0.130 ***	0.003	0.000
lnNC	0.105 ***	0.003	0.000	0.095 ***	0.005	0.000	0.108 ***	0.108	0.000

** Significant at 5% and *** Significant at 1%.

, as these are of interest in adaptation studies. This study used the Driscoll and Kraay (D/K) standard error method to examine the association between dependent (ADC) and explanatory (RED, EF, GDP, FDI, RE, NC) variables for all countries under study. The adaptation readiness indicator includes social, governance, economic willingness, and the efficient use of investment to adapt to climate change demonstratinging a significantly positive impact on adaptive capacity. The rise in adaptive capacity was 0.375% (LMICs), 0.575% (UMICs), and 1.070% (HICs) for a 1% increase in readiness. This demonstrates that higher-income countries such as Australia, Austria, Belgium, Canada, Denmark, France, Finland, Germany, Iceland, Norway, New Zealand, Sweden, Switzerland, USA, and the UK are highly ready countries; therefore, their adaptive capacity is high [10].

The upper-middle-income countries, including Argentina, Azerbaijan, Brazil, China, Dominican Republic, Mexico, Panama, and Thailand, displayed a medium readiness level [57]. In contrast, the low-income countries, Bangladesh, Cote D’lvoire, Ghana, Myanmar, and Vietnam, with a very low level of readiness, had a lower capacity to adapt [6,10]. These results are in line with previous studies. This implies that the lack of adaptive capacity and readiness in lower and upper-middle-income countries severely impedes the pursuit of low-carbon development, excluding higher-income countries.

The results showed adverse and significant effects of the ecological footprint on adaptive capacity. A possible way to lower adaptive capacity is to increase the footprint size. More precisely, a 1% influence on the ecological footprint will cause a reduction in the adaptive capacity by −0.178% (LMICs), −0.434% (UMICs), and −0.238% (HICs). However, in the case of developed countries (HICs), the effect of climate change was negative but insignificant because their growth rate is high. Additionally, most already have large footprints. Many alternative solutions and adaptation efforts are using de-carbonization technologies that diminish CO₂ emissions; For example, fuel switching, renewable energy, and carbon storage and utilization [134,135]. The opposite may be true for developing and emerging countries; an ecological footprint increase may be crucial to strengthen their economies. Still, as advanced technology may not widely exist, their adaptive capacity remains low [77]. Similar to the levels of emissions, the footprint size is larger, and the heavier strain on the environment seems to correlate negatively with adaptive capacity.

The results showed a significant negative impact of economic growth on adaptive capacity, implying that a 1% increase in economic growth will cause a reduction in adaptive capacity by −0.200% (LMICs) and −0.043% (HICs). In contrast, the effect of economic growth was positive and significant for the upper middle-income panel, providing a 0.292% increase in adaptive capacity. Theoretically, climate change influences economic growth by altering productivity/income levels or reducing a country’s capacity to adapt. The implication is that adaptation capacity is determined by the level of development [136]. This indicates that economic development produces varied results; it alters the exposure and sensitivity of developing countries to climate change and lowers the country’s adaptation capacity. In some cases, the effect may be adverse if development is concentrated in risky areas like flood plains. In others, the effects will be positive, for instance, climate-sensitive agriculture diversification [60,84].

The FDI inflow was favorable to adaptive capacity in the case of UMICs and HICs; for a 1% increase in foreign investment inflow, the increase in adaptive capacity was 0.008% (UMICs) and 0.022% (HICs). Contrarily, a negative and insignificant effect of the FDI was only found for LMICs. Adaptation, and particularly adaptive capacity, requires resources and financing that are often unavailable in developing countries. FDI is considered a primary source of finance in mitigating environmental damage and adapting to the climate change process, especially when foreign investments emanate from cleaner or more efficient technologies [92,93]. Several countries are now focusing on the so-called “Green” FDI that internalizes the negative environmental externalities related to industrial production. These developments capture the potential of FDI to directly contribute to fostering adaptive capacity [89,96]. Renewable energy significantly contributes to increasing capacity-building opportunities for innovative practices. The increase in adaptive capacity was 0.046% (LMICs), 0.081% (UMICs), and 0.130% (HICs) due to a 1% increase in the use of renewable energy. It is suggested to expand investments in renewable technologies, support vital adaptation measures for the most vulnerable communities, and to analyze the country’s/region’s adaptive capacity [58]. Renewable energy is imitative of natural resources, and it can replenish itself. The literature has revealed the positive role of renewable-energy consumption in climate change adaptation [137,138].

Investing in countries’ natural capital creates simultaneous benefits for the planet and people and has a net-positive impact on adaptive capacity. Investing in nature was found to be favorable in capacity building for LMICs, UMICs, and HICs as the rise in adaptive capacity was 0.105% (LMICs) and 0.095% (UMICs), and 0.108% (HICs) for a 1% increase in investment in natural capital. Investing in natural capital is critical for meeting selected nature-related UN Sustainable Development Goals (SDGs 6, 13, 14, 15) and creating “a better and more sustainable future for all [139]. Today’s world leaders are investing a paltry USD 60.79 billion in natural capital, including China, Spain, the United States, South Korea, Canada, the UK, Germany, India, Trinidad and Tobago, France, Pakistan, and Chile [140].

5.6. Panel Granger Non-Causality (JKS) Results

The JKS panel heterogeneous non-causality test proposed by [54] was performed to explore the linkages of adaptive capacity with adaptation readiness, climate change (EF), economic development (GDP), foreign direct investment, renewable energy, and natural capital investment in all three panels (

Table 7. Pairwise non-causality test results.

No.	Null Hypothesis (H0)	HPJK Wald Statistic	p-Value	HPJK Coefficient	p-Value	Causality Direction
Lower-Middle Income
1	RED ≠ ADC	2.794 *	0.094	−0.085 *	0.095	Unidirectional
2	ADC ≠ RED	1.659	0.197	0.008	0.198
3	EF ≠ ADC	69.096 ***	0.000	−0.048 ***	0.000	Unidirectional
4	ADC ≠ EF	0.004	0.983	−0.002	0.983
5	GDP ≠ ADC	25.111 ***	0.000	−0.030 ***	0.000	Bidirectional
6	ADC ≠ GDP	3.367 *	0.066	0.080 *	0.067
7	FDI ≠ ADC	10.187 ***	0.001	−0.003 ***	0.001	Bidirectional
8	ADC ≠ FDI	2.778 *	0.095	0.598 *	0.096
9	RE ≠ ADC	0.061	0.804	−0.009	0.805	No causality
10	ADC ≠ RE	1.193	0.999	0.001	0.999
11	NC ≠ ADC	4.647 **	0.031	0.008 **	0.031	Unidirectional
12	ADC ≠ NC	2.488	0.114	0.006	0.115
Upper-Middle Income
1	RED ≠ ADC	9.376 ***	0.002	−0.038 ***	0.002	Bidirectional
2	ADC ≠ RED	7.546 ***	0.006	−0.163 ***	0.006
3	EF ≠ ADC	10.469 ***	0.001	−0.028 ***	0.001	Unidirectional
4	ADC ≠ EF	0.004	0.948	0.007	0.949
5	GDP ≠ ADC	29.910 ***	0.000	−0.031 ***	0.000	Unidirectional
6	ADC ≠ GDP	0.321	0.570	−0.078	0.570
7	FDI ≠ ADC	0.471	0.492	−0.005	0.492	Unidirectional
8	ADC ≠ FDI	10.896 ***	0.001	1.878 ***	0.001
9	RE ≠ ADC	3.937 **	0.047	−0.191 **	0.047	Unidirectional
10	ADC ≠ RE	0.206	0.649	0.002	0.650
11	NC ≠ ADC	13.478 ***	0.000	0.019 ***	0.000	Bidirectional
12	ADC ≠ NC	5.437 **	0.019	0.214 **	0.020
High Income
1	RED ≠ ADC	26.525 ***	0.000	−0.132 ***	0.000	Bidirectional
2	ADC ≠ RED	6.432 **	0.011	−0.028 **	0.011
3	EF ≠ ADC	40.147 ***	0.000	0.193 ***	0.000	Unidirectional
4	ADC ≠ EF	0.603	0.437	0.009	0.437
5	GDP ≠ ADC	11.685 ***	0.001	−0.070 ***	0.001	Bidirectional
6	ADC ≠ GDP	24.413 ***	0.000	0.098 ***	0.000
7	FDI ≠ ADC	9.801 ***	0.001	−0.002 ***	0.002	Bidirectional
8	ADC ≠ FDI	3.766 *	0.052	0.941 *	0.052
9	RE ≠ ADC	5.314 **	0.021	−0.011 **	0.021	Bidirectional
10	ADC ≠ RE	28.407 ***	0.000	−0.296 ***	0.000
11	NC ≠ ADC	8.622 ***	0.003	0.0402 ***	0.003	Bidirectional
12	ADC ≠ NC	21.624 ***	0.000	0.177 ***	0.000

*** Significant at 1%; ** Significant at 5%; * Significant at 10%.

). This study tested the hypothesis of Granger causality and feedback causality, i.e., y being causal to x and x being causal to y, to approximate the direction of causality among concerned variables that causes the adaptive capacity in their respective panel. Fascinatingly, the empirical results revealed a unidirectional causality association running from ecological footprint to adaptive capacity. This depicts that climate change (including climate variability and extremes) impacts the ability of a system to adjust to moderate potential damages. This assessment is also consistent with the outcomes of the long-run estimation, which is more important for adaptation as well as policymaking authorities. This result is analogous to the findings of [10] for the 192 UN countries and [77] for eight different world economies.

Moreover, the bidirectional causality observed between adaptation readiness and adaptive capacity (UMICs and HICs) shows validation of the feedback hypothesis between these variables. Results aligned with [6,10] for 51 African countries. They found that enhancing adaptive capacity to climate change can enhance countries’ preparedness to mitigate the effects of climatic extremes, and nations with high adaptation readiness are more adaptable to climate hazards. The feedback hypothesis shows bi-directional causality between (a) economic growth and adaptive capacity in LMICs and HICs, (b) FDI and adaptive capacity in LMICs and HICs, (c) renewable energy and adaptive capacity in HICs, and (d) natural capital and adaptive capacity in UMICs and HICs. Results also showed a unidirectional causality running from (a) adaptation readiness to adaptive capacity in LMICs, (b) economic growth to adaptive capacity in UMICs, (c) adaptive capacity to FDI in UMICs, (d) renewable energy to adaptive capacity in UMICs, and (e) natural capital to adaptive capacity in LMICs (Figure 5). These causality associations suggest that these indicators significantly influence the adaptive capacity in all three panels, as increasing adaptive capacity is a practical means of coping with climate uncertainties.

6. Conclusions and Policy Implication

Climate change vulnerability is a widely acknowledged reality. In line with the existing literature, developing countries have identified natural resource exploitation, poor food and water resources, inappropriate infrastructures, and inadequate adaptation capacities. As such, these countries are the most vulnerable to climatic extremes, while economic, social, and governance commitments are proactive, with a high capacity to adapt in the case of developed countries. This study empirically examined the adaptation readiness, climate change, and economic development nexus across different income groups using panel data from 115 countries from 1995 to 2020. This study incorporated two indices (i.e., adaptive capacity and adaptation readiness) in the adaptation framework. The Driscoll and Kraay (D/K) technique was employed for regression analysis to control for heterogeneity among panels. This contribution shifted the attention from assessing the drivers, impacts, and damages caused by climatic extremes to assessing the adaptation actions. The results demonstrated that developed countries have a more remarkable ability to deal with climate risks i.e., HICs (4.0501). In contrast, developing countries, such as UMICs (3.8555) and LMICs (3.3851), have a limited adaptive capacity. The findings also show that adaptation readiness significantly positively affects adaptive capacity across three income groups. For a 1% increase in readiness, the gain in adaptive capacity was 1.070% (HICs), 0.575% (UMICs), and 0.375% (LMICs). However, the lack of adaptive capacity and readiness in lower- and upper-middle-income countries severely impedes the pursuit of low-carbon development, excluding higher-income countries. Finally, the JKS causality findings disclosed the existence of a one-way as well as a two-way causality among the variables. The feedback hypothesis showed bi-directional causality for economic growth, foreign direct investment, renewable energy, and natural capital to adaptive capacity in UMICs and HICs. This causality association suggests that these indicators significantly influence the adaptive capacity in all three panels. Therefore, developing adaptive capacity is a practical means of coping with climate uncertainties, including variability and extremes. The enhancement of adaptive capacity and preparedness for climate change “hot spots” has been understood as an important adaptation strategy to reduce vulnerabilities.

The empirical outcomes have important policy implications, specifically in climate-sensitive countries. Formulating and implementing nationwide or possibly regional agendas with measures to facilitate suitable adaptation to climate change is recommended. Developing countries need to build a solid adaptive capacity and adapt the three pillars of adaptation readiness to mitigate the risks of climate change, while the higher-income countries must prioritize wetland conservation and disaster preparedness. However, the goal of economic development to mitigate the consequences of climate shock requires financial assistance and technological resources to enhance local capacity to design and implement successful adaptation strategies. This study has a few limitations, calling for further research. First, this study did not consider the impact of funding resources. Climate finance is a critical ingredient in the efforts to build countries’ adaptive capacity and also plays an essential role in mitigation. The pledges made by developed countries to finance climate change have not been realized yet. Secondly, the emphasis should move from assessing vulnerability, community adaptation potential (adaptive capacity and adaptation readiness), and resilience to the depth of actual implementation of adaptation strategies to attain sustainable development.