The Effect of Innovation on Climate Resilience in Developing Countries: Evidence from a Panel Quantile Regression Approach

Abstract

This study examines the effect of innovation on climate resilience in developing countries, covering annual data from 2008 to 2022, with a focus on how this relationship varies across different levels of vulnerability. The primary purpose is to understand whether innovation contributes uniformly to climate resilience or if its impact differs depending on a country’s resilience status. Addressing this question is crucial for developing evidence-based and context-specific climate policies. To capture these heterogeneous effects, this study employs a panel quantile regression approach using data from developing countries. This method allows the estimation of the influence of innovation proxied by the Global Innovation Index (GII) and the climate resilience Index. The findings show that innovation has a consistently positive and statistically strong impact on climate resilience across all quantiles, with the strongest impact at the median. The results carry important policy implications. Firstly, developing countries should prioritize innovation-driven strategies to strengthen resilience across different climate risk profiles. Secondly, policies supporting renewable energy deployment should target countries with higher emissions to maximize their impact. Thirdly, fiscal tools, such as environmentally aligned tax policies, should be emphasized particularly in more vulnerable contexts. Finally, trade policies, population dynamics and integration of climate finance variables must be integrated into climate strategies to enhance long-term sustainability.

1. Introduction

Climate change is a global challenge, posing severe risks to social, economic and ecological systems worldwide. Its impacts are particularly profound in developing countries, where weak infrastructure, limited adaptive capacity and persistent poverty amplify vulnerability to climate-related shocks (Hallegatte et al., 2016; IPCC, 2021). Empirical evidence shows that these nations often face disproportionate losses from climate disasters, which hinder their development prospects and exacerbate existing inequalities (Stern, 2004). Although international agreements have emphasized the urgency of mitigation and adaptation, the resilience gap between developed and developing economies continues to widen (Dell et al., 2012; UNFCCC, 2015). Strengthening climate resilience in the Global South, therefore, requires not only external support but also the mobilization of internal drivers of transformation.

Around the world, many public sectors have implemented policies aimed at environmental protection. For instance, in France, all registered companies are mandated to submit reports outlining how their operations might positively or negatively affect the environment. Global climate change initiatives consistently emphasize the urgency of addressing environmental challenges and reducing greenhouse gas emissions. Key international agreements such as the 1992 United Nations Framework Convention on Climate Change, the 1997 “Kyoto Protocol, the 2012 Doha Amendment, and the 2016 Paris Agreement have established shared commitments for both developed and developing countries to lower greenhouse gas emissions by curbing carbon production and consumption. Climate change affects all aspects of life on a global scale, with concerns growing, especially, in developing nations as population growth accelerates and urban areas expand rapidly (Tyler & Moench, 2012). These impacts manifest in various ways, including significant decreases in rainfall and rising average temperatures due to long-term climate shifts (Al-Maamary et al., 2017). Additionally, extreme weather events and catastrophic shocks lead to severe damage and disruption to urban infrastructure, including buildings, energy and water supply systems, roads, bridges, and transport networks (Crick et al., 2018; Howe, 2011; Marks & Thomalla, 2017; US EPA, 2020).

Over time, individuals and urban infrastructure become increasingly vulnerable to disruptions caused by climate-related impacts. These challenges underscore the importance of evaluating the consequences, strengthening adaptive capacities, and improving the resilience of infrastructure systems at risk (Ayyub, 2018). Understanding and identifying the impacts of climate change is crucial for all nations (Watson et al., 1997). Additionally, preparing for potential hazards through effective communication and stakeholder engagement plays a significant role (Ryan et al., 2020). To support adaptation efforts and promote resilience, numerous governments and organizations have developed standards, manuals, guides and other resources aimed at addressing anticipated climate change impacts (Tyler & Moench, 2012; CRC-URI & IRG, 2009).

Previous studies investigating the impacts of climate change and the strategies for adaptation have primarily focused on assessing vulnerability using “qualitative and quantitative approaches (Harvey et al., 2014; Thornton et al., 2008). Developing countries remain particularly exposed to the adverse consequences of climate change, often facing significant economic losses, deepening poverty and population displacement. However, relatively few studies have examined climate resilience, especially, in connection with innovation, rather than limiting the focus to vulnerability alone (Keil et al., 2008). In climate change research, vulnerability and resilience are closely related yet distinct concepts. Vulnerability refers to the degree of sensitivity and the capacity to adapt before a natural shock occurs, whereas resilience reflects how households or communities respond to and recover from such shocks afterward. A household is considered resilient when it demonstrates reduced vulnerability over time and successfully recovers from climate-related disturbances (Perez et al., 2015). Developing reliable indicators of vulnerability and resilience is therefore crucial for designing adaptation strategies that minimize the negative impacts of climate variability including droughts and floods on communities. However, constructing these measures, particularly, at the household level, remains difficult due to the scarcity of high-frequency panel data capable of linking climate events to socio-economic outcomes. Understanding the drivers of vulnerability and the mechanisms that foster resilience can help address structural weaknesses and guide effective resource allocation for long-term adaptive transformation.

Globally, ecological unpredictability results from climate change, posing a significant risk to the long-term viability of human society. Climate change is primarily caused by human actions. It can result in various significant effects, including rising sea levels, altered weather patterns and decreased agricultural output. The only effective method for adequately preparing for climate change is through climate resilience. This can only be achieved through major progress in innovation and an increase in the adoption of clean technologies, resulting in enhanced clean energy efficiency. Additionally, it may be beneficial to attempt to recover or prepare for climate change. Consequently, developed and developing nations should focus more on climate resilience to effectively prepare for and recover from climate change through innovation. Climate change is a main concern for government officials and economic policymakers in addressing the urgent requirements of the Sustainable Development Goals. Droughts, wildfires, intense rainfall with short spans, loss of biodiversity, desertification and melting glaciers are a few of the results of climate change. Owing to the severe effects on the environment, the world has focused on innovations to address modern challenges. Previous research indicates that innovation is a crucial factor capable of providing a sustainable solution to climate change.

Existing studies including some by Wang et al. (2022), Yu and Du (2019), Fethi and Rahuma (2020), Hashmi and Alam (2019), Töbelmann and Wendler (2020), Ahmad et al. (2023) and Teklu et al. (2023), have progressive understanding of innovation’s impact on environmental performance and adaptation at the sectoral or micro level. Limited attention has been paid to its broader influence on national climate resilience using comprehensive measures of innovation. Consequently, macro-level empirical evidence on the relationship between national innovation capacity and climate resilience remains scarce. Methodologically, most of the existing studies have employed mean-based estimation techniques including Ordinary Least Squares (OLS), Fixed Effects (FE), or Generalized Method of Moments (GMM) (Usman & Hammar, 2021; Villanthenkodath & Mahalik, 2022), which capture only the average effects of innovation. These approaches fail to reflect the heterogeneous nature of developing economies, where countries at different resilience levels may experience distinct innovation impacts. Therefore, this study seeks to bridge this critical knowledge gap by analyzing the effect of innovation on climate resilience in developing countries.

The contribution of the study is in twofold, firstly, unlike earlier studies that employ narrow proxies, this study uses broader indicators such as the Global Innovation Index (GII) as a measure of innovation and the ND-GAIN Resilience Index as a measure of climate resilience in a macro level. This study provides new insights into the extent to which innovation contributes to resilience in vulnerable economies. Secondly, methodologically, the study applies a panel quantile regression analysis that allows for an estimation of the conditional distribution, as opposed to the conditional mean in ordinary least squares (OLS)-based approaches. This allowed the study to examine the link across quantiles and, hence, the effects of innovation on climate resilience across developing countries. Unlike systems that are based only on mean values such as pooled ordinary Least Squares (POLS), Fixed Effects (FE), and Random Effects (RE) models, the quantile method estimates different points on the conditional distribution of the chosen dependent variable (Cade & Noon, 2003). Hence, the study seeks to answer crucial research questions: does innovation significantly enhance climate resilience in developing countries?

The structure of this paper is organized as follows: Section 2 presents the theoretical foundations and reviews relevant empirical studies on the relationship between innovation and climate resilience. Section 3 outlines the data sources, variable measurements and econometric methods employed in the analysis. Section 4 presents estimation results. Section 5 discusses and interprets the empirical findings, while Section 6 concludes the study by summarizing key insights and offering practical policy recommendations.

2. Literature Review

The section explores theoretical and empirical literature on innovation on resilience. The review structured into two main categories, focusing separately on studies conducted in developing and developed countries. Theoretical perspectives provide a foundation for understanding how innovation contributes to climate resilience, whereas empirical studies offer evidence-based insights into this relationship.

2.1. Theoretical Literature

The theoretical foundation of this study rests on the relationship between innovation and climate resilience, emphasizing how technological, institutional, and economic mechanisms interact to enhance adaptive capacity in developing countries. Innovation and climate resilience are interconnected through multiple theoretical perspectives that explain how technological progress, knowledge diffusion and institutional capacity influence a country’s ability to adapt to climate shocks. This section discusses the key theories that underpin the relationship between innovation and climate resilience, and these include the Endogenous Growth Theory, the Schumpeterian Innovation Theory and the Adaptive Capacity Framework.

The Endogenous Growth Theory, established by Romer (1990), emphasize that technological advancement and knowledge accumulation are internal drivers of long-term economic growth. Unlike traditional neoclassical models, which treat technological progress as an external factor, this theory posits that innovation results from purposeful investment in human capital, research and development (R&D) as well as knowledge spill overs. In the context of climate resilience, innovation stimulates the creation of cleaner technologies, renewable energy solutions, and adaptive systems that strengthen an economy’s capacity to cope with climate-related risks. Developing nations that allocate resources to R&D, education, and technology transfer are better positioned to reduce environmental vulnerability and promote sustainable growth (Aghion & Howitt, 1992; Grossman & Krueger, 1991).

Schumpeter (1934) viewed innovation as the fundamental force of “creative destruction,” where new technologies replace outdated ones, driving productivity and transformation across sectors. According to this perspective, firms that engage in innovative activities enhance their competitiveness and stimulate structural change within an economy. Applied to climate resilience, the Schumpeterian view suggests that green innovations such as renewable energy systems, efficient industrial processes and sustainable urban technologies disrupt carbon-intensive systems and pave the way for resilient growth. These transitions, while initially disruptive, yield long-term adaptive benefits and sustainable competitiveness. The Adaptive Capacity Framework provides a conceptual foundation for understanding how nations build resilience to climate shocks. It highlights the role of: institutions, governance quality, social capital and innovation in shaping adaptive responses to environmental challenges. Innovation enhances adaptive capacity by providing technological solutions that mitigate vulnerability, improve resource efficiency, and enable more effective disaster response systems. Countries that integrate innovation into their climate governance strategies through research networks, technological partnerships and policy support develop stronger institutional mechanisms to withstand and recover from climate disturbances (Smit & Wandel, 2006; Nelson et al., 2007).

Bringing these theoretical perspectives together, innovation operates as both an economic growth driver and an adaptive mechanism against climate change. Endogenous growth theory explains the role of innovation investment, Schumpeterian theory captures its transformative effect, and the adaptive capacity framework situates innovation within broader environmental and institutional dynamics. This integrative framework positions innovation as a central tool for achieving both climate resilience and sustainable development in developing economies. Comprehensive, reviewed literature states that innovation plays a transformative part in enhancing climate resilience, yet its success depends on the synergy among technological advancement, policy coherence and institutional capacity. By addressing these interdependencies, developing economies can leverage innovation, not only, to adapt to environmental changes but also to pursue sustainable growth pathways. The conceptual model in Figure 1 illustrates this relationship, showing how innovation interacts with control variables renewable energy, taxation, trade, GDP and population to affect resilience outcomes in developing countries.

2.2. Empirical Literature

Empirical research exploring the link between innovation and climate change outcomes has produced mixed evidence. While several studies identify innovation as a major enabler of environmental sustainability and resilience, others suggest that its impact can be negative or context-dependent particularly in developing countries where industrial expansion often precedes environmental safety.

2.2.1. Studies Showing a Positive Relationship Between Innovation and Climate Resilience or Environmental Quality

Developed Countries

In developed countries, Agan (2025) explored the effects of climate change adaptation and clean energy innovations on economic growth across OECD countries from 1990 to 2020. Using a system-GMM approach, the study found that climate change adaptation, clean energy innovations, and socio-political development indicators significantly and positively influence economic growth. Moreover, interactions between adaptation efforts and temperature changes further enhanced long-term output performance. Likewise, Guo et al. (2021) conducted empirical analyses integrating innovation policies with renewable energy and climate adaptation strategies. Their results showed that technological progress leads to reduced emissions and greater climate resilience when supported by cohesive innovation policies.

Developing Countries

In developing countries, Teklu et al. (2023) examined climate-smart agricultural innovations among Ethiopian smallholder farmers through a field study focusing on technological and institutional innovations. Their findings demonstrated that these innovations significantly enhanced household adaptive capacity and resilience. Similarly, Ahmad et al. (2023) used panel data analysis across developing countries to show that innovation contributes to lower carbon emissions by improving energy efficiency and encouraging cleaner production methods. Khayat (2023) applied a social justice framework to highlight how social and technological innovations support vulnerable communities in coping with extreme weather and building resilient institutions, addressing climate injustice and inequality. Further studies by Zameer et al. (2020), Mensah et al. (2018), and Yu and Du (2019) confirmed that innovation promotes sustainable growth and environmental quality by diffusing green technologies and improving resource efficiency, particularly when backed by sound institutional quality and policy incentives.

2.2.2. Studies Reporting a Negative or Mixed Relationship Between Innovation and Environmental Outcomes

Developing Countries

Several studies in developing countries report that innovation may have negative or mixed environmental impacts, often linked to weak governance and insufficient green technology infrastructure. Including, Xiao and Fei (2024) applied a two-way fixed-effects model to data from 131 countries between 1995 and 2019 and found that climate vulnerability significantly hinders green innovation, especially in non-OECD, lower-income, and less-globalized economies. In addition, Khan et al. (2020) and Villanthenkodath and Mahalik (2022) analyzed sectoral data in emerging economies and concluded that innovation increased carbon emissions primarily because technological advancement was concentrated in energy-intensive sectors. National-level studies by Dauda et al. (2019) and Usman and Hammar (2021) further demonstrated that innovation not targeted at cleaner technologies contributes to environmental degradation in the Global South. Additionally, Demir et al. (2020) for Turkey and Santra (2017) India found through time-series analysis that innovation correlated with long-term increases in $\mathrm{C}\mathrm{O}$₂ emissions, indicating economic growth often takes precedence over environmental sustainability.

2.2.3. Emerging Consensus

Overall, empirical evidence suggests that innovation can either strengthen or weaken climate resilience depending on its orientation and policy environment. Where innovation is aligned with green investment, renewable-energy transitions and institutional quality, it tends to enhance resilience. However, in economies driven by carbon-intensive industrialization and weak regulation, innovation may initially exacerbate environmental vulnerability. This duality underscores the need to distinguish between “green innovation” that supports adaptation and mitigation and “brown innovation” that intensifies environmental stress.

By analyzing the relationship between innovation and resilience across different levels of vulnerability, this study broadens the discussion to a wider cross-country context, highlighting how the impact of innovation differs among developing economies with varying degrees of resilience. Prior studies examining the innovation-climate change connection have produced mixed results, often due to differences in methodologies, samples and innovation measures. Many investigations in both developed and developing countries have relied on narrow proxies such as patent counts or R&D expenditures for innovation. In contrast, this study utilizes the Global Innovation Index (GII), offering a more holistic assessment of innovation capabilities. Additionally, this research employs two key indices; namely, the GII and the ND-GAIN Resilience Index to strengthen the robustness of its findings. By applying a quantile regression method, the study captures the heterogeneous effects of innovation at different resilience levels, addressing a methodological gap in previous work and contributing uniquely to the literature.

Drawing from the reviewed theoretical and empirical literature, the study develops a conceptual model to illustrate the hypothesized relationships between innovation and climate resilience in developing countries. The model assumes that innovation, measured by the Global Innovation Index (GII), enhances national adaptive capacity, and thus, improves climate resilience (RI). However, this relationship may also be influenced by several control variables, including environmental tax (TAX), renewable energy consumption (RENE), trade openness (TRA), population growth (POP) and economic growth (GDP). Each of these factors is expected to affect resilience either directly or indirectly. The conceptual framework model in Figure 1 summarizes these relationships and provides the basis for the hypotheses tested in the subsequent methodology and analysis sections.

As illustrated in Figure 1, innovation is anticipated to be a key driver in enhancing climate resilience., either directly or through complementary factors such as: renewable energy adoption, environmental taxation, trade integration and economic growth. Population growth is anticipated to exert a negative effect by increasing exposure and resource pressure. The framework therefore provides the empirical foundation for testing the proposed hypotheses, which examine the magnitude and direction of these relationships across developing countries, using a panel quantile regression approach.

2.2.4. Hypothesis of the Study

Innovation and Climate Resilience

Innovation expands a country’s capability to anticipate, absorb, and recover from climate shocks. Through technological advancement, economies can adopt climate-smart systems, improve early-warning mechanisms, enhance energy efficiency, and promote adaptive capacity. Positive evidence suggests that innovation drives environmental improvement and resilience by reducing emissions, improving productivity, and strengthening institutions. Studies such as Amegavi et al. (2021) and Guo et al. (2021) show that innovation fosters adaptation and reduces vulnerabilities when integrated with strong policy frameworks.

However, some studies report neutral or even negative effects of innovation when institutions are weak or when technology diffusion is limited. In developing countries, capacity gaps may prevent innovation from translating into resilience benefits.

Hypothesis:

H₁. 

Innovation (GII) positively influences climate resilience (RI) in developing countries.

Renewable Energy Consumption and Climate Resilience

Renewable energy use enhances resilience by reducing dependence on fossil fuels, lowering emissions, and improving environmental quality. Evidence shows that renewable energy improves climate sustainability and reduces vulnerability to shocks (Bhattacharya et al., 2016). Moreover, renewable energy investments support green technological innovation and diversify energy systems. On the other hand, in some low-income regions, renewable infrastructure may be insufficient or unreliable, potentially limiting resilience gains in the short term.

Hypothesis:

H₂. 

Renewable energy consumption (RENE) enhances climate resilience.

Environmental Taxation and Resilience Outcomes

Environmental taxes aim to reduce pollution and incentivize cleaner production. In well-designed systems, taxes encourage innovation, improve environmental quality, and strengthen adaptive capacity. When tax revenues are reinvested into climate-smart projects, resilience improves. However, poorly structured taxes can impose economic burdens on households and firms, potentially reducing adaptive capacity. Some studies find mixed or ambiguous results depending on tax design, enforcement, and institutional quality.

Hypothesis:

H₃. 

Environmental tax (TAX) has an ambiguous effect on resilience depending on its design, implementation, and reinvestment.

Trade Openness and Climate Resilience

Trade openness may strengthen resilience by facilitating access to cleaner technologies, international best practices, and resources that support adaptive capacity. Through global integration, economies may benefit from technology spill overs that enhance resilience. Conversely, trade exposure may increase vulnerability if countries become dependent on climate-sensitive sectors, experience external shocks, or face “pollution-haven” effects where weak regulation undermines environmental quality (Dean et al., 2017).

Hypothesis:

H₄. 

Trade openness (TRA) can either improve or weaken climate resilience depending on exposure, structural composition, and adaptive capacity.

Population Growth and Climate Resilience

Population growth increases pressure on land, water, food systems, and public services. Rapid demographic expansion can heighten vulnerability by straining infrastructure and natural resources. Studies in developing regions consistently show that population pressures reduce resilience and intensify exposure to climate risks (Amegavi et al., 2021). While some argue that larger populations can provide labour resources, the evidence leans strongly toward negative impacts in low-income countries.

Hypothesis:

H₅. 

Population growth (POP) negatively affects climate resilience due to increased resource pressure and vulnerability.

Economic Growth and Climate Resilience

Economic growth can strengthen resilience by improving infrastructure, increasing adaptation investment, and enhancing institutional capacity. Wealthier economies typically possess stronger absorptive and adaptive capabilities, enabling more effective responses to climate shocks. However, when growth is driven by carbon-intensive activities, resilience may weaken due to environmental degradation. Most empirical evidence supports a positive role of growth in enhancing resilience, especially when accompanied by structural transformation.

H₆. 

Economic growth (GDP) strengthens climate resilience through improved adaptive and absorptive capacity.

These six hypotheses provide a structured foundation for the empirical analysis. Each hypothesis aligns with theoretical expectations, empirical findings, and the study’s conceptual model and will guide the modelling strategy in the subsequent chapters.

3. Materials and Methods

3.1. Model Specification

In this section, we present and discuss the empirical model and data employed in this paper. The study adopted and modified the models used by: Udeagha and Ngepah (2022), Raghutla and Chittedi (2023) and Basty and Ghachem (2023). We specify the empirical model as follows:

$${lnRI}_{it}={\beta }_0+{\beta }_1{lnGII}_{it}+{\beta }_{2}ln{TAX}_{it}+{\beta }_{3}ln{RENE}_{it}{+{\beta }_{4}ln{TRA}_{it}+{\beta }_{5}ln{POP}_{it}+{\beta }_{6}ln{GDP}_{it}+{\mu }_{it}+\epsilon }_{it}$$

(1)

where ${\beta }_0$ is the intercept, ${\beta }_{1,}{\beta }_{2,}{\beta }_3{,\beta }_{4,}{\beta }_{5,}{\beta }_6$ are explanatory variables coefficients, and ${\epsilon }_{it}$ is the error term.

where

• Resilience index, (RI) as a measure for climate resilience;
• GII measure for innovation;
• TAX measure for environmental tax;
• RENE-renewable energy;
• TRA is trade;
• POP Population;
• GDP is Gross Domestic Product (measure for economic growth);
• µ_it country specific effect.

The study expects ${\beta }_1$ > 0, because innovation and climate resilience enhance a country’s capacity to develop adaptive technologies, by building resilient infrastructure, and respond to climate shocks effectively. Lee and Min (2015) highlighted the importance of innovation in advancing sustainability by showing that green R&D not only drives eco-innovation but also leads to reductions in carbon emissions and improvements in firm performance.

Environmental tax ${\beta }_2$ levy imposed on activities or products that harm the environment, aimed at reducing pollution and encouraging sustainable practices, ${\beta }_2$ is expected to be positive. A well-designed environmental tax can encourage polluters to adopt cleaner technologies, and this shift in behaviour can enhance national climate resilience. Although, in low-income settings, high taxation without reinvestment in climate programmes could be burdensome.

Renewable energy ${\mathsf{\beta }}_3$ generated from natural, replenishable sources including solar, wind, hydro and geothermal power, ${\mathsf{\beta }}_3$ > 0 expected to positive, mitigates greenhouse gas emissions, and promotes long-term sustainability, and strengthen resilience. Bhattacharya et al. (2016) found a strong positive link between renewable energy consumption and sustainability in developing economies. Then, Destek and Sarkodie (2019) highlighted that renewable energy consumption contributes to CO₂ mitigation and resilience.

Trade ${\beta }_4$ the import and export of goods and services between countries, reflecting the exchange of products across borders, ${\mathsf{\beta }}_4>0$ expected to be positive to provide access to green technologies and adaptive goods, but can also increase environmental stress through emissions-intensive production and consumption. Antweiler et al. (2001) argue that trade improves environmental quality through scale and technique effects. Dean et al. (2017) warned that trade liberalization can worsen environmental conditions in weak regulatory settings.

Population growth ${\beta }_5$ an increase in the number of individuals in a given area over time, influencing economic and environmental dynamics, ${\mathsf{\beta }}_5$ < 0 is expected to be negative where rapid population growth can strain infrastructure, increase exposure to environmental hazards, and hinder adaptive capacity. Shahbaz et al. (2014) associated population growth with rising emissions and vulnerability. Amegavi et al. (2021) confirmed that population pressures reduce resilience in sub-Saharan African countries. GDP ${\mathsf{\beta }}_6$ the total monetary value of all final goods and services produced within a country over a specific period, serving as a key indicator of economic activity, ${\mathsf{\beta }}_6$ > 0 where higher income levels increase a country’s fiscal space to invest in resilient infrastructure, disaster preparedness, and green technologies. Fankhauser and McDermott (2014) note that economic development improves adaptive capacity. Abid et al. (2016) found that richer nations are better able to cope with climate shocks.

3.2. Data Description and Limitations

This research utilizes yearly data from 2008 to 2022. The study period and country selection were guided by the accessibility and completeness of data across countries. Variables such as innovation proxies (such as, patents, R&D, scientific journals and medium-high technology exports), readiness index and environmental tax are available only in few developing countries. While, some indicators, such as climate finance or institutional capacity, were excluded due to data gaps, potentially omitting relevant channels through which innovation affects resilience. While the methods employed address several empirical concerns, the analysis remains constrained by data availability. The final sample consists of 24 developing countries for which continuous and comparable data were obtainable over the study period. These include Azerbaijan, Brazil, Botswana, China, Cameroon, Colombia, Costa Rica, Egypt, Ghana, Kazakhstan, Kenya, Morocco, Madagascar, Mexico, Mali, Mauritius, Namibia, Panama, Peru, Senegal, Tunisia, Uganda, Uruguary and South Africa shown in Appendix A. The reason for selecting this 24 developing is due to the availability of consistent data. Variables used in the estimation are presented in

Table 1. Data description.

Variable Type	Variables	Variable Description	Expected Signs	Sources
Dependent variable	Resilience index, (RI) (measure for climate resilience)	ND-GAIN for vulnerability and Readiness	Dependent variable	ND-GAIN website https://gain.nd.edu/our-work/country-index/ (Accessed on 31 March 2025)
Main-independent variable	GII (measure for innovation)	Global Innovation index country’s overall innovation capacity based on input factors including institutions, human capital, infrastructure and investment and output factors, including knowledge creation, technology adoption, and creative production.	Positive	World Intellectual Property (WIPO) https://www.wipo.int/web-publications/global-innovation-index-2025/en/gii-2025-at-a-glance.html (Accessed on 31 March 2025)
Control variables	TAX	Environmental tax (% of GDP)	Positive	Organization for Economic Co-operation and Development (OECD)
	RENE	Renewable energy consumption (% of total final energy consumption)	Positive	World Development Indicators (WDI)
	TRA	Trade (% of GDP)	Positive	World Development Indicators (WDI)
	POP	Population (% of total population)	Negative	World Development Indicators (WDI)
	GDP (measure for economic growth)	Gross Domestic Product per capita (constant US$)	Positive	World Development Indicators (WDI)

Source: Compiled by author (2025).

, below.

3.3. Global Innovation Index

In this study, innovation, the primary variable of interest, is measured using the Global Innovation Index (GII), which was produced by the World Intellectual Property Organization (WIPO). The GII offers a comprehensive evaluation of a country’s innovation performance by combining a diverse set of indicators that capture both innovation inputs such as human capital, research infrastructure and institutional quality and innovation outputs, including knowledge creation, technological progress and the production of creative goods and services (WIPO, 2024). Its multidimensional design allows for meaningful comparisons across countries, capturing how effectively economies convert knowledge and resources into measurable innovative results. This makes the GII well-suited for assessing the contribution of innovation to: economic resilience, sustainability and competitiveness, especially, within developing nations.

Prior studies (Y. Chen et al., 2022; Naifar, 2023) have extensively used the GII as a reliable indicator of national innovation capacity. These works highlight the index’s broad scope and consistent methodology as strengths that support its use in analyzing the links between innovation, environmental performance, and macroeconomic stability. Building on this foundation, the current study adopts the GII as a robust, internationally recognized proxy for innovation.

3.4. Climate Change Resilience Index

In this study, climate resilience is the dependent variable and is measured using the ND-GAIN resilience index from the ND-GAIN database (C. Chen et al., 2015). This index offers a comprehensive evaluation of a country’s preparedness and capacity to manage climate-related risks and shocks (ND-GAIN, 2025; Wu et al., 2024). It encompasses multiple aspects of resilience, such as economic stability, infrastructure quality and institutional effectiveness, providing a well-rounded assessment of national adaptive capacity. By integrating indicators from environmental, social and governance sectors, the Readiness Index reflects both vulnerabilities and strengths that affect a country’s ability to respond to climate change (ND-GAIN, 2025; C. Chen et al., 2015). Its broad, multidimensional approach makes it a suitable tool for exploring the interaction between climate risks and wider economic and institutional factors.

Previous studies, including those by Boitan and Marchewka-Bartkowiak (2022) and Volz et al. (2020), highlight the usefulness of this measure in empirical research. Their findings show that the Readiness Index effectively reflects a country’s adaptive capacity, governance quality, and mechanisms for managing vulnerability. Consistent with these studies, this research employs the Readiness Index as a reliable and widely accepted proxy for climate resilience, given its ability to capture cross-country differences in climate preparedness and adaptive performance.

3.5. Estimation Technique

The Panel Quantile Regression Model

Introduced by Koenker and Bassett (1978), the panel quantile regression model is a statistical method used to analyze the heterogeneous impacts of explanatory variables across diverse points (quantiles) of the conditional distribution of a dependent variable while accounting for both cross-sectional and time-series dimensions in panel data. Unlike traditional mean-based regression such as ordinary least squares, which focuses on the average effect, quantile regression explores how relationships vary at the tails or other specific quantiles (for example, the 10th, 25th, 50th, and 90th percentiles), making it particularly useful for studying phenomena with non-uniform impacts of green innovation on climate resilience across diverse economic or environmental conditions.

The quantile regression estimates the conditional quantile of the dependent variable as a function of explanatory variables. For a given quantile τ (where 0 < τ < 1), it minimizes the weighted absolute deviations rather than squared errors. In extending the analysis to panel data, the dataset tracks observations for multiple entities (for example, countries or firms) across several time periods. The panel quantile regression model incorporates this structure, which allows for individual-specific heterogeneity such as fixed effects while examining distributional effects. The panel quantile regression model by Koenker and Bassett (1978) was adopted as follows:

$$Q_{y_{it}}\left(\mathsf{\tau }\right|X_{it,}{Z}_{i,t,}{\alpha }_i)={\alpha }_i+{\beta }_1\left(\mathsf{\tau }\right)·GII{+\beta }_2\left(\mathsf{\tau }\right)·Z_{it}$$

(2)

where

• $y_{it}$: ND-GAIN resilience index for entity (i) at time (t);
• τ: Quantile of interest [for example, $\tau$ = 0.10, 0.25, 0.50, 0.75, and 0.90.];
• ${GII}_{it}$: Innovation measure (Global Innovation Index);
• $X_{it}:$ Main explanatory variables (environmental tax, renewable energy, trade, population and GDP per capita);
• $Z_{it}$: Controls variables or additional variables (carbon emissions, urbanization index, food production index, Environmental degradation index);
• ${\alpha }_i:$ Fixed effects for unobserved heterogeneity, for example, country-specific factors;
• ${\beta }_1\left(\mathsf{\tau }\right):$ Quantile-specific effect of innovation on resilience;

The key feature is that $\beta \left(\mathsf{\tau }\right),$ differs across quantiles, revealing how the impact of ${GII}_{it}$ on $y_{it}$ varies and might have a stronger effect on resilience in highly vulnerable regions (lower quantiles) than in less vulnerable ones (higher quantiles). The empirical estimation of panel quantile regression involves solving an optimization problem tailored to the quantile of innovation on resilience, while addressing the panel structure. The estimation is conducted by minimizing the quantile loss function using the following Equation (3), where for a given quantile τ, the estimator minimizes:

$${}_{{\alpha }_i,\beta \left(\mathsf{\tau }\right)}{}^{min}\sum _{i=1}^N\sum _{t=1}^T{p}_{\tau }(y_{it}-{\alpha }_i-{X^{\prime }}_{it}\beta \left(\mathsf{\tau }\right))+\lambda \sum _{i=1}^N|{\alpha }_i|$$

(3)

where

• The “min” indicates that we are minimizing the objective function with respect to the parameters ${\alpha }_i$ (the individual fixed effects) and $\beta \left(\mathsf{\tau }\right)$, (the quantile-specific coefficients). In mathematical optimization, “min” means we are searching for the values of ${\alpha }_i$ and $\beta \left(\mathsf{\tau }\right)$ that make the entire expression as small as possible;
• ${\alpha }_i,\beta \left(\mathsf{\tau }\right)$: This specifies the parameters over which the minimization is performed. Then, adjusting ${\alpha }_i$[one for each individual (i)] and $\beta \left(\mathsf{\tau }\right),$(the vector of coefficients for the $\mathsf{\tau }$-th quantile)] to find the optimal values that minimize the objective function;
• $“p_{\tau }\left(u\right)=u\left(\mathsf{\tau }-\mathrm{I}\left(\mathrm{u}<0\right)\right):$ To check the function” which assigns asymmetric weights to residuals:

  ✓

  Weight $\mathsf{\tau } \mathrm{i}\mathrm{f} \mathrm{u}\ge 0 \left(\mathrm{o}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{p}\mathrm{r}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\right)$

  ✓

  Weight $\left(1-\mathsf{\tau }\right) \mathrm{i}\mathrm{f} \mathrm{u}<0 \left(\mathrm{u}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{r}\mathrm{p}\mathrm{r}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\right)$

• I (.): Indicator function;
• $\Lambda$: Penalty parameter (cross validation);
• $\left|{\alpha }_i\right|$: is the absolute value of the individual fixed effect (i).

To address the incidental parameters problem arising from the inclusion of many fixed effects, a penalization technique is applied to shrink the individual effects toward zero, improving efficiency and avoiding overfitting. Standard errors are obtained using bootstrap methods to account for the non-smooth nature of the quantile loss function and the panel structure of the data. This approach allows for robust estimation of distributional effects while controlling for time-invariant unobserved heterogeneity across units.

4. Results

This section presents estimation results.

Table 2. Descriptive Statistics.

Variables	obs	Mean	Std. dev.	Min	Max
RI	405	0.3953074	0.0855946	0.2415415	0.5777789
GII	405	26.36911	13.19525	2.02	55.3
TAX	405	1.306669	0.7528398	0.0326322	3.184
l_RENE	405	3.198254	0.9471312	0.0953102	4.546481
l_TRA	405	4.153725	0.4155681	3.095848	5.22182
POP	405	1.321495	1.148335	−2.450719	3.490596
GDP	405	2.027401	3.988106	−18.94217	15.06954

Source: Compiled by author (2025).

provides informative, summary statistics or descriptive statistics of the indicators of the relationship between innovation and climate resilience for selected developing countries from 2008 to 2022, innovation (GII) having the highest mean value of 26.36911, followed by trade with about 4.153725. Whereas, RI (ND-GAIN resilience index) is having the lowest mean value of 0.3953074. Log-transformations were applied to renewable energy and trade to reduce skewness, stabilize variance, address scale heterogeneity across countries, and enable elasticity-based interpretation of coefficients. These transformations improve model fit and robustness in the panel quantile regression framework and align with established empirical practice.

Table 3. Correlation analysis.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) RI	1.000
(2) GII	0.285 ***	1.000
(3) TAX	0.281 ***	0.102 **	1.000
(4) l_RENE	−0.529 ***	−0.141 ***	−0.002	1.000
(5) l_TRA	0.397 ***	0.002	0.230 ***	−0.275 ***	1.000
(6) POP	−0.806 ***	−0.248 ***	−0.354 ***	0.365 ***	−0.286 ***	1.000
(7) GDP	0.170 ***	0.077	0.078	−0.071	0.097 *	−0.192 ***	1.000

*** p < 0.01, ** p < 0.05, * p < 0.1.

outlines the level of correlation between all variables used in this study. The indicator that showed highest correlation is trade coefficient of 0.397 and significant and renewable energy being the lowest at −0.002.

Table 3 presents the correlation coefficients among the study variables used to examine innovation and climate resilience in developing countries. The correlation matrix provides an overview of the degree and direction of association among pairs of variables. It reveals that climate resilience is positively associated with innovation, taxation and trade openness, suggesting that higher innovation and economic activity enhance adaptive capacity. In contrast, population growth exhibits a strong negative correlation with resilience, indicating that demographic pressure may hinder climate adaptation. The remaining variables show moderate relationships but provides no evidence of multicollinearity; by so doing, confirming the suitability of the variables for regression analysis. Inclusively, these relationships provide a preliminary understanding that motivates further econometric analysis to establish causality. Overall, the correlation results align with the theoretical expectations of this study.

Innovation, trade openness and effective environmental policies appear to strengthen a country’s adaptive capacity, while population pressures and limited renewable energy utilization tend to undermine it. These preliminary associations suggest that innovation plays a vital role in shaping climate resilience, but its effects are intertwined with demographic and structural factors. Consequently, the subsequent econometric analysis explores whether these relationships remain significant when controlling for other macroeconomic variables and country-specific characteristics, thereby providing a more robust assessment of the influence of innovation on climate resilience in developing economies. Although some variables show moderate-to-strong pairwise correlations particularly between population and resilience none of the correlations exceeds the conventional multicollinearity threshold (0.85). This suggests that the variables can be jointly included in the regression model without severe multicollinearity concerns.

The estimated results show (

Table 4. Panel quantile regression results.

Dependent Variable (RI) Resilience Index	Q10	Q25	Q50	Q75	Q90
GII	0.0006 **	0.0005 ***	0.0006 **	0.0006 *	0.0006 **
	(2.3241)	(4.2845)	(2.453)	(1.8165)	(2.3194)
TAX	−0.0055	0.0011	0.0041	0.0036	−0.0055 **
	(−1.3398)	(0.3832)	(0.8972)	(0.7248)	(−2.1273)
l_RENE	−0.0367 ***	−0.0144 ***	−0.0145 ***	−0.0332 ***	−0.0367 ***
	(−6.3932)	(−6.2109)	(−3.8175)	(−6.7849)	(−7.3563)
l_TRA	−0.0093	0.0498 ***	0.05 ***	−0.0009	−0.0093
	(−1.0152)	(11.4283)	(6.1839)	(−0.0734)	(−1.0303)
POP	−0.0564 ***	−0.0457 ***	−0.0463 ***	−0.0516 ***	−0.0564 ***
	(−16.9309)	(−17.3665)	(−12.5194)	(−11.379)	(−22.0363)
GDP	−0.0002	−0.0006	0.0001	0.0001	−0.0002
	(−0.2737)	(−0.8633)	(0.202)	(0.0723)	(−0.3733)
Constant	0.6792 ***	0.2493 ***	0.2672 ***	0.5883 ***	0.6792 ***
	(12.5944)	(11.1076)	(6.7336)	(9.207)	(13.7122)
Observations	401	401	401	401	401
Pseudo R2	0.5093	0.5382	0.5045	0.5039	0.5093

Note: t-values are in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1.

) evidence on how innovation and other macroeconomic factors influence climate resilience across different levels of the resilience distribution. Quantile estimates for the resilience index (RI) at $\tau$ = 0.10, 0.25, 0.50, 0.75, and 0.90. The findings reveal heterogeneous relationships across the resilience distribution and point to a consistent role for innovation alongside important interactions with policy and structural variables. Innovation, measured by the Global Innovation Index (GII), shows a positive and statistically significant effect on resilience across all quantiles. This suggesting that innovation consistently enhances adaptive capacity, aligning with endogenous growth and Schumpeterian theories, which posit that technological progress fosters efficiency, institutional readiness and adaptive development. The persistence of this positive effect across quantiles indicates that innovation benefits both low- and high-resilience economies, though the impact appears more pronounced in moderately resilient countries.

Tax is statistically insignificant at most quantiles, although it shows a negative and significant effect at the 0.90 quantile. This pattern suggests that environmental or corrective taxation has few detectable associations with resilience in lower-resilience countries, while in the most resilient group higher tax levels are associated with lower measured resilience. A plausible interpretation is that in high-resilience settings, taxes may reflect stronger regulatory burdens or transition costs. For example, fiscal measures that temporarily constrain adaptive investment, or alternatively, they proxy for a policy mix where taxation alone is insufficient without complementary spending on adaptation. The finding highlights that fiscal instruments interact with institutional context and their net effect on resilience is conditional on a country’s capacity to convert fiscal resources into adaptation outcomes. In developing economies, tax increases often fall on productive sectors with limited ability to absorb additional costs. Studies such as those by Gemmell et al. (2011) and Arnold et al. (2011) highlight that certain tax structures can suppress innovation, slow capital accumulation, and reduce growth-enhancing activities, particularly where institutional quality is weak or limited. This mechanism can generate a negative short-run association between taxation and climate or economic resilience.

The renewable energy (l_RENE) displays a significant negative coefficient throughout all quantiles, implying that the short-term transition toward renewable energy may initially strain resilience, possibly due to high implementation costs, infrastructural challenges, or dependence on traditional energy systems. However, this finding also reflects the transitional dynamics predicted by environmental Kuznets-type relationships, where early investments in cleaner energy temporarily reduce efficiency before generating long-term sustainability gains. However, recent evidence suggests that renewable energy can have short-term negative effects on resilience in developing countries because early-stage deployment often carries significant integration, infrastructure and financing challenges. High upfront investment requirements, intermittent supply, and weak grid systems may temporarily reduce overall system stability and economic readiness. Studies such as Destek and Sarkodie (2019) and Demir et al. (2020) show that in low-income contexts, renewable energy expansion can initially impose adjustment costs before long-run benefits appear. Additionally, countries with lower resilience often invest more aggressively in renewables as a policy response to climate vulnerability, creating a reverse causality pattern where higher renewable shares are observed in already fragile environments. These dynamics help explain why the coefficient for renewable energy appears negative despite its recognized long-term advantages.

Trade openness (l_TRA) has a positive and statistically significant relationship with resilience at the 0.25 and 0.50 quantiles, suggesting that countries with moderate resilience levels benefit from increased trade integration through technology transfer and resource diversification. The effect weakens at higher quantiles, consistent with the notion that highly resilient economies may have already internalized the benefits of openness. Trade openness supports resilience by enabling access to diversified inputs, advanced technologies, and global knowledge flows. Openness also allows economies to adjust more effectively during shocks by reallocating resources and tapping international markets. Studies including Frankel and Rose (2002) and Crespo Cuaresma and Feldkircher (2013) found that trade integration enhances economic stability and accelerates technology diffusion, which strengthens adaptive capacity. The literature supports the result showing a positive and significant relationship between trade and resilience across the distribution.

Population growth (POP) exerts a strong and negative effect across all quantiles, confirming that demographic pressure undermines adaptive capacity by straining infrastructure, resources and governance systems. This aligns with neo-Malthusian perspectives emphasizing that rapid population growth can exacerbate vulnerability to climate shocks. Empirical research, such as Barrios et al. (2010), shows that population pressures can heighten vulnerability to climate impacts, reduce per capita resource availability, and weaken responsiveness to shocks. These structural constraints help explain why population has a negative association with resilience in your results.

Lastly, GDP shows an insignificant effect across quantiles, indicating that economic growth alone does not directly translate into resilience gains without targeted innovation and institutional support. Overall, the results reinforce that innovation serves as a significant driver of climate resilience in developing economies, but its effectiveness depends on complementary factors including renewable energy integration, trade participation and population management. In these cases, higher GDP does not automatically translate into improved adaptive capacity. Literature on the “growth environment trade-off” shows that early-stage growth in developing countries often intensifies exposure to climate risks through pollution, urban congestion, and infrastructure deficits (Grossman & Krueger, 1995; Dasgupta et al., 2002). Similarly, studies on structural transformation argue that without parallel investment in innovation and governance, GDP growth may not enhance and can even temporarily reduce resilience. These findings provide empirical backing to structural transformation theories that emphasize innovation-led, inclusive and sustainable growth pathways.

The multi-quantile perspective helps decision-makers understand how climate resilience drivers vary across different performance levels. The insights can be used to craft tiered and targeted climate adaptation policies in developing countries. Figure 2 shows how each independent variable’s influence on climate resilience changes across different quantiles (levels of climate resilience). Below, is a visual summary of the quantiles.

The above Figure 2 illustrates the trends in regression coefficients of various predictors across the $\tau$ = 0.10, 0.25, 0.50, 0.75 and 0.90. Each line in the chart represents a variable’s effect on climate resilience at different levels of resilience (low, middle and high). The quantile results show that innovation measured by Global Innovation Index, positively and significantly, enhances climate resilience across all quantiles, confirming that technological advancement strengthens adaptive capacity in developing countries. Tax has a weak or negative effect at higher quantiles, implying that fiscal measures alone may not translate into resilience unless effectively reinvested. Renewable energy (l_RENE) displays a consistent negative sign, possibly reflecting transitional adjustment costs or reverse causality. Trade openness (l_TRA) supports resilience mainly at middle quantiles, while population growth (POP) consistently reduces resilience, suggesting that demographic pressures weaken adaptive capacity. GDP remains insignificant, indicating that income levels, alone do not guarantee climate resilience.

Sensitivity Analysis

The study investigated how innovation influences climate resilience in developing countries. To capture variations across nations, panel regression analysis was conducted using FE and RE estimation techniques.

Table 5. Hausman Test Results.

Chi-squared	122.16
p-value	0.000

Source: Compiled by author (2025).

shows how the Hausman test was then applied to determine the appropriate model specification. The results present the chi-square statistic of 122.16 with a p-value of 0.000, leading the study to reject the null hypothesis, indicating that the null hypothesis of no systematic transformation among FE and RE estimators is rejected. Therefore, the Fixed Effects model is preferred, confirming that unobserved country-specific effects are correlated with the explanatory variables.

Table 6. Fixed and Random Effects Results with Hausman Test for climate resilience.

Variables	Fixed Effects	Random Effects
GII	0.0002 **	0.0003 **
	(2.1351)	(2.4711)
TAX	−0.003	0.0008
	(−0.7392)	(0.1967)
l_RENE	0.0032	−0.0274 ***
	(0.3287)	(−4.3829)
l_TRA	−0.0456 ***	−0.0189 **
	(−4.8896)	(−2.1001)
POP	−0.0005	−0.0221 ***
	(−0.1069)	(−5.5242)
GDP	0.0003	−0.0002
	(0.8931)	(−0.6288)
constant	0.5725 ***	0.5827 ***
	(11.4424)	(12.8871)
Observations	401	401

Note: t-values are in parentheses *** p < 0.01, ** p < 0.05.

shows the Hausman test results which confirm that the Fixed Effects estimator is appropriate for this analysis. GII exhibits a positive and statistically strong effect “(${\beta }_1$ = 0.0002, p < 0.05)” on climate resilience, suggesting that innovation enhances adaptive capacity. Trade openness and population growth have negative and significant effects, implying that external exposure and demographic pressures reduce resilience. GDP and taxation remain insignificant, indicating that fiscal and economic growth factors alone are insufficient to improve resilience without innovation-driven adaptation mechanisms.

The findings align with previous studies such as Teklu et al. (2023) and Ali et al. (2023), which highlight innovation as the main driver of adaptive capacity. The negative association between trade openness and resilience underscores the risks of external vulnerability, while the insignificant role of GDP and tax suggests that policy interventions should emphasize technological innovation and institutional readiness rather than mere fiscal expansion. Overall, the Fixed Effects model confirms that resilience in developing economies is shaped by structural and innovation-specific factors, justifying its use for this study. The results show how the magnitude and precision of the effects vary across the resilience distribution, highlighting stronger and more statistically robust impacts at higher quantiles. In addition, the quantile results were compared with a standard mean-based fixed-effects model. The comparison confirms that relying solely on mean effects would obscure important distributional differences, as the fixed-effects estimates capture only the average relationship, while the quantile approach reveals substantial variation across countries with different resilience levels.

Endogeneity is a potential concern in estimating the relationship between innovation and climate readiness, particularly due to simultaneity and omitted institutional factors that may influence both variables. To mitigate this, the analysis incorporates lagged values, allowing the model to capture the delayed effect of innovation while reducing reverse causality. This approach is consistent with empirical studies noting that innovation impacts resilience over time rather than contemporaneously. In addition, the use of fixed effects Table 6 helps control for unobserved, time-invariant country characteristics such as governance quality, structural capacity, or geographic exposure. The quantile regression framework also contributes to reducing endogeneity bias by capturing heterogeneous effects across the distribution, which limits the influence of extreme values or structural shocks concentrated in specific countries. While more advanced techniques such as GMM or instrumental variables were considered, they are less suited to the threshold quantile design and the relatively short time dimension of the dataset. Nonetheless, the combined use of lagged regressors, fixed effects, and distribution-sensitive estimation provides a reasonable safeguard against endogeneity concerns in this context.

5. Discussion

The results support hypothesis 1 indicating that, innovation measured by GII exerts a strong positive influence on climate resilience in different quantiles. The finding aligns with prior research by Yu and Du (2019) and Teklu et al. (2023). ${\mathrm{H}}_2$ is partially supported: renewable energy shows a negative effect, indicating transitional challenges in developing countries. ${\mathrm{H}}_2$ is partially supported; renewable energy usage exhibits a significant negative effect across quantiles, suggesting possible transitional challenges or short-term adaptation costs associated with renewable energy deployment. ${\mathrm{H}}_3$ receives limited support since environmental taxes are mostly insignificant, except for a negative impact at the highest quantile, indicating that fiscal policies may have adverse effects in contexts of already high resilience.

Hypothesis 4 conditionally supported the following: trade openness shows positive effects primarily at lower and median quantiles, but it is insignificant elsewhere, implying that trade integration benefits countries with moderate resilience, while its advantages may diminish at extremes. ${\mathrm{H}}_5$ is confirmed, as higher population size consistently reduces resilience, possible due to increased resource pressures. Lastly, ${\mathrm{H}}_6$ is not supported; GDP does not significantly influence resilience, highlighting that economic scale alone does not guarantee improved adaptive capacity. These results align with resilience theory, which underscores the importance of innovation and adaptive capacity rather than solely economic wealth in fostering climate resilience.

6. Conclusions

The purpose of this study is to analyze how innovation influences climate resilience in emerging nations by employing a panel quantile regression method. This multi-quantile perspective helps decision-makers to understand how climate resilience drivers vary across different performance levels. The insights can be used to craft tiered and targeted climate adaptation policies in emerging nations. The chapter summarize innovation on climate resilience across different quantiles for the resilience index (RI) at $\tau$ = 0.10, 0.25, 0.50, 0.75, and 0.90. The analysis indicates that GII consistently improves resilience, suggesting that innovation has a significant positive impact on high-performing (resilient) nations. This supports our primary hypothesis, demonstrating that innovation consistently aids climate resilience across all quantiles in chosen developing countries. Evidence suggests a beneficial connection between innovation and climate resilience, reinforcing the primary hypothesis. Observation shows that innovation acts as a crucial factor in fostering growth and reducing environmental harm (Razzaq et al., 2021). Furthermore, the response of innovation to the rise in climate change is also significant to certain research, including studies by (Su & Moaniba, 2017; Wang et al., 2022). This aligns with endogenous growth and Schumpeterian innovation theories, which emphasize the transformative role of technological progress in sustaining long-term development.

However, the negative effects of trade openness and renewable energy indicate transitional dynamics where economies face short-term adjustment costs, infrastructure gaps, and vulnerability to external shocks before realizing the full benefits of innovation-led adaptation. This outcome aligns with the structural change theory which posits that developing economies often experience temporary instability as they shift from traditional to innovation-driven systems. The insignificant influence of taxation and GDP underscores that fiscal measures and income levels alone do not guarantee climate resilience without strategic investment in innovation systems and effective policy implementation. This might imply inefficient application of tax revenues in developing nations, and tax policies may not effectively focus on innovation or resilience. The implication of this status quo is that, in developing countries with low resilience, taxation might obstruct climate adaptation initiatives. Ineffective execution or improper distribution of tax funds may prevent the tax from achieving its intended objectives. However, GDP might indicate total economic activity, but does not specifically pertain to climate resilience or innovation. While wealth might not result in effective infrastructure adaptation or sound policies.

Developing nations frequently experience inequality; thus, average wealth does not necessarily indicate robust resilience and may suggest that increased income by itself does not lead to improved climate resilience. In developing nations, institutional elements such as corruption and poor governance might diminish the anticipated beneficial impacts of the macroeconomic variables examined in the research. In this study, increasing population size consistently diminishes resilience, with higher population numbers negatively associated with climate resilience, due to resource pressure and urban weaknesses. Where larger populations consistently diminish resilience, emphasizing the significance of urban planning and infrastructure enhancement.

Policy recommendations among others include the allocation of funds towards research and development while advocating for technology-driven approaches to address climate issues, the promotion of collaboration among academia, government and industry to ensure that innovations are reachable and expandable in areas with low resilience. To the above, the following should be added: the exploration of the adverse effects of renewable energy on resilience by focusing on operational inefficiencies and elevated transition expenses, the combination of the implementation of renewable energy with skills development and improvements in infrastructure. These should be accompanied by the enhancement of infrastructure and minimization of inefficiencies throughout the energy transition, addressing of trade-climate risks and formulation of trade strategies that minimize vulnerability to climate disruptions and the promotion of local resilience as well as the diversification of supply chains.

In addition to the above, policy makers and authorities should encourage eco-friendly city development and construction; they should increase funding in healthcare, education and housing, create adaptation policies that consider population needs to enhance public services and infrastructure for handling climate stress, additionally, they should re-evaluate tax and GDP effect pathways. Among nations with low resilience, there should be a creation of tax benefits for investments that promote climate resilience, as well as the enhancement of public financial management to guarantee efficient utilization of tax income. Beyond GDP, there should be an emphasis of structural preparedness as well as the enhancement of GDP growth through investments in social safety nets and environmental infrastructure. More focused fiscal strategies that direct funds towards resilience should be explored. This should then be followed by a synchronization of the strategies for economic growth with climate objectives. If implemented, these insights can assist policymakers in pinpointing crucial leverage points to enhance climate resilience in developing nations, especially, among those that are already doing relatively well. Including, actionable policy steps by strengthening public–private partnerships to support innovation deployment, expanding access to climate-finance instruments, and investing in digital and institutional capacity that enables countries to absorb and utilize new technologies. Governments can also encourage co-funded research programmes, promote private-sector investment in adaptive technologies, and develop targeted incentives.

Limitations and Future Research

This study is limited by data availability on innovation and resilience indicators across developing countries, which constrained the sample period and coverage. The use of secondary indices may also mask within-country disparities. Future research could extend this work by using micro-level or regional data, applying dynamic panel quantile techniques, and incorporating institutional quality and climate–finance indicators to better capture long-term adaptive processes. Comparative studies between developed and developing countries, as well as sector-specific case studies, would also help clarify contextual differences. Finally, the unexpected negative effect of renewable energy warrants further investigation across regions to understand the conditions under which renewable investments strengthen or weaken climate resilience.