Introduction

Climate change poses significant social, economic, and environmental risks to South Africa, particularly regarding water availability and quality. Since 2015, South Africa has experienced severe droughts, leading to significant losses in agriculture and restricted usage of water, exacerbating food insecurity. Furthermore, the increase in mean temperatures, which almost doubled from 0.55 to 1.03 °C between 2008 and 2016 (https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/global/time-series/globe/land_ocean/1/1/1850-2023), has had significant impacts on various ecosystems and it is feared that the world is on track towards breaching the 1.5 °C target specified in the Paris Agreement on climate change.

The potential effects of climate change on South Africa’s Fisheries sector are also of great concern. Changes in ocean temperatures driven by climate change could lead to the alteration of the migration patterns of fish and a potential decline in fish stocks in South African marine waters (Potts et al., 2015). Rising temperatures also pose a threat to biodiversity, as fish naturally have a thermal preference for maximizing their physiological processes and functionality (Cockcroft et al., 2008). Furthermore, climate change may also cause variations in water salinity, thus affecting fish species in different geographical areas (Muringai et al., 2021). The impact of economic development on the fisheries sector is also significant, as it can lead to overfishing, habitat degradation, pollution, and other environmental impacts that can affect fish populations (Rashdan et al., 2021).

As the fisheries sector is a major source of nutrients, sustenance and livelihood in poor African households, it plays a crucial role in addressing poverty, hunger and food security issues in line with the UN’s Sustainable Development Goals (SDGs) (Smallhorn-West et al., 2022). The sector is also essential for job creation, particularly for local coastal communities that depend on the ocean for their survival and livelihood (Sowman et al., 2014). Unfortunately, marine resource depletion and overexploitation have remained pressing issues in the country, which have been exacerbated by recent climate change conditions (Ortega-Cisneros et al., 2021). To address these challenges, the Department for Environment, Forestry, and Fisheries (DEFF) has recently intensified its efforts to safeguard marine ecosystems and biodiversity by expanding the network of marine protected areas (MPAs), promoting the development of aquacultures as a sustainable alternative to wild-captures Fisheries and introducing Small-Scale Fisheries Policies to protect the rights of small-scale fishermen (Sowman, 2011; Purdon et al., 2020).

We present an empirical investigation into the effects of climate change and economic development on the fisheries sector in South Africa. Traditionally, the environmental Kuznets curve (EKC) has served as a theoretical framework for studying the relationship between economic activity and environmental (such as carbon emissions) or ecological footprint (such as built-up land, cropland, fishing ground, grazing land and deforestation). More recent studies have extended this framework to examine the link between economic activity and fisheries catches (Madhoo, 2011; Aydin et al., 2019; Peng et al., 2020; Rashdan et al., 2021; Phiri and Tembo, 2023). Other studies have also investigated the impact of climate change on fisheries catches using various ecological and bioeconomical and models (see Huang et al., 2021 for a review) and yet empirical evidence for South Africa remains scarce. Our study contributes to the literature by investigating the multivariate time–frequency relationship between economic development, temperature changes, and fisheries catches in South Africa from 1960 to 2020.

To carry out our empirical analysis, we use a set of continuous wavelet tools in a three-step approach. Firstly, we use wavelet power spectrum plots (WPS) to examine the evolution of the individual time series in a time–frequency space. Secondly, we use wavelet coherence analysis to investigate the time–frequency co-movements between temperatures and fisheries stock and between temperatures and fisheries catches. Lastly, we use partial wavelet coherence (PWC) to investigate the time-frequency co-movement between (i) economic development and fisheries stock while controlling for temperatures and between (ii) temperatures and fisheries catches while controlling for economic development.

Our WPS findings indicate that the variables under examination exhibit similar energy distribution at low frequencies, albeit displaying some variation at higher frequencies. Furthermore, our wavelet coherence analysis reveals a positive association between economic development and fisheries catches, a U-shaped relationship between temperatures and fisheries catches, and a humped or inverted U-shaped relationship between temperatures and economic development. Lastly, our PWC analysis suggests that the U-shaped (inverted U-shaped) relationship between temperatures and fisheries catches (economic development) remains unaltered when controlling for economic development (fisheries stock), whereas the GDP-fisheries relationship turns largely insignificant when controlling for temperatures.

Overall, the findings from our study highlight the stronger influence of global temperatures over economic development in affecting the trajectory of Fisheries catches in South Africa. Whilst higher economic development in South Africa has facilitated higher Fisheries catches, our results show that these effects can be overshadowed by rising temperatures which have been decreasing per capita GDP since 2010. Conversely, rising temperatures have only had a negative effect on Fisheries catches until its turning point in 2010, when these adverse effects disappeared implying that the South African Fisheries industries have become more resilient to climate change.

Literature review

The present study is grounded in the theoretical framework of the environmental Kuznets curve (EKC), which posits a non-linear relationship between economic development and environmental degradation (Grossman and Krueger, 1991, 1995). The EKC suggests that during a country’s early stages of development, the economy is reliant on the use of dirty energy sources, leading to a positive relationship between growth and emissions (scale effects). However, as the economy progresses from an industrial-based to a knowledge-based economy driven by technology and artificial intelligence, it becomes more reliant on clean energy sources, resulting in a positive relationship between growth and emissions due to technical and substitution effects. Notably, previous research has examined the EKC in the context of CO2 emissions for South Africa, with some studies supporting the theory (Udeagha and Ngepah, 2019; Udeagha and Muchapondwa, 2022; Saba, 2023a, b), while others present contradictory evidence (Nasr et al., 2015; Ganda, 2019). Moreover, the EKC has been criticized for assuming that environmental pollution is not cumulative, and its effects are reversible, whilst in reality, CO2 emissions cause cumulative environmental pollution, and biodiversity loss is irreversible.

The application of the EKC to the fisheries sector has been deemed more suitable because depleted fish stocks can be recovered if exploitation rates are reduced, and the population abundance of fisheries remains high enough to induce an Allee effect. Several studies have investigated the fisheries-based EKC, including Madhoo (2011), who used marine fish production as an ecological proxy and found an inverted U-shaped relationship with GDP per capita for Mauritius. Aydin et al. (2019) used fishing grounds footprint as an ecological factor in the EKC for 26 European countries and found an inverted U-shaped relationship for the panel, while Peng et al. (2020) examined the quadratic and cubic relationships between fisheries economy and marine environment in 12 Chinese coastal regions and found an inverted N-shape relationship in East China and Yellow Sea, whereas an inverted U-shape relationship was found for South China Sea and Bohai regions. Rashdan et al. (2021) recently verified an N-shaped fisheries-based EKC for 14 emerging economies, with South Africa being the only African country in the panel.

Notably, the existing literature has provided different theoretical explanations for the different shapes of the Fisheries-based EKC. For instance, Madhoo (2011) and Peng et al. (2020) describe the traditional inverted U-shaped Fisheries EKC as consisting of ‘scale’ effects that arise at earlier stages of economic development when the economy is dependent on agriculture and large-scale fishing, and growth of the population and income at this stage leads to more fisheries catch. However, when economic development surpasses a certain level, technical effects take over and fish catch is eventually discouraged, pollution affecting biodiversity is abated, which results in increased fish exports and higher income growth. Rashdan et al. (2021) identify a third phase of development in which composition and technical effects occur after crossing the second turning point. At this stage, the economy is characterized by stricter environmental regulations, cleaner industries and technologies, and improved R&D activity, all of which lead to better resource management of fisheries and innovations that improve the fisheries catch without damaging the aqua ecosystem. On the other hand, there exist other explanations in the literature for the U-shaped and inverted N-shaped relationship. For instance, Peng et al. (2020) explain the U-shaped Fisheries EKC as consisting of (i) inverse scale effects that arise due to high water pollution experienced during the earlier stages of development consequentially leading to decreasing fisheries catch before the first turning point, (ii) technical effects occur after crossing the first turning point in which water pollution is improved and consequentially fish stock increases alongside income and (iii) composition effects which occur after crossing the second threshold due to a shift from traditional agriculture and fishing sectors to manufacturing and services oriented industries.

Currently, the scientific knowledge on the fisheries-based EKC is still in its nascent stages, and our study aims to expand on the existing empirical literature in two ways. Firstly, previous studies have used economic activity as an instrument that degrades the ecological environment as suggested by the traditional EKC theory, while our study proposes the use of temperatures as a more direct measure of climate change. Second, we differ methodologically from previous studies by employing wavelet coherence analysis, which allows for the examination of amplitude and phase dynamics in the synchronization between variables within a time-frequency domain by decomposing the time series into a time-frequency space and providing localized time-frequency information on the series. Overall, wavelet coherence analysis overcomes criticisms of traditional estimators used in previous studies, such as their inability to capture time-varying and/or frequency-varying synchronizations amongst the data which can arise in the form of structural breaks or other unobserved nonlinearities.

Methods and data description

Methods

The majority of econometric models employed in the related literature have relied on linear regression estimators, which can only provide information regarding the sign (positive or negative) or magnitude (weak or strong correlation) of the co-movement between two-time series. Other models such as cointegration and causality models—including VAR, VECM, and ARDL—have been utilized in the general EKC literature to differentiate between short-term and long-term cointegration effects, and provide insights into the causal relationships between the series. Certain nonlinear versions of these models can also capture location asymmetries, such as quantile regression models used in the studies of Allard et al. (2018), Sharif et al. (2020) and Shahzad et al. (2021). Additionally, models such as the threshold autoregressive (TAR) and smooth transition regression (STR) models can be used to model structural breaks, as demonstrated by Tatoglu and Polat (2021). Despite these advances in econometric modelling techniques, the current methods do not provide an inclusive framework that can simultaneously address asymmetries arising from time and frequency variation in the data.

Wavelet analysis can be considered a potential solution to the deficiencies presented by traditional estimators. Morlet et al. (1982a, b) introduced wavelets as a set of mathematical functions that can decompose a signal in a scale-by-scale manner. These tools have been widely employed to investigate the time-frequency properties of geological data such as cyclones and temperature data (Lau and Weng, 1995). Torrence and Compo (1998) introduced the concept of wavelet coherence to describe the co-movement between two decomposed time series in time–frequency space by using convolution operations whereas Mihanovic et al. (2009) introduced partial wavelet coherence analysis in order to investigate time–frequency co-movement between pair of time series whilst controlling for the influence of other variables. However, it was Aguiar-Conraria and Soares (2011) who popularized the use of wavelet coherence among economists and social scientists. A detailed discussion of the applications and uses of complex wavelet tools in economics is presented in Aguiar-Conraria and Soares (2014).

In our study, we utilize the three-step procedure described in Aguiar-Conraria and Soares (2014) to investigate the relationship between temperatures (x1), economic development (x2) and Fisheries stock (y) in time-frequency space. Firstly, we convolute the individual time series with a set of complex-valued ‘daughter wavelets’ which are generated by a common ‘mother’ wavelet. The convolution process generates the wavelet coefficients that are responsible for the amplitude and phased dynamics in time–frequency space. The daughter wavelets for each series are defined as

$$W_y\left( {s,\tau } \right) = \mathop {\smallint }\nolimits_{ - \infty }^\infty y\left( t \right)\frac{1}{{\sqrt s }} \,*\, \left( {\frac{{t -\tau }}{s}} \right){\rm {d}}t$$
(1)
$$W_{x1}\left( {s,\tau } \right) = \mathop {\smallint }\nolimits_{ - \infty }^\infty x1\left( t \right)\frac{1}{{\sqrt s }} \,*\, \left( {\frac{{t -\tau }}{s}} \right){\rm {d}}t$$
(2)
$$W_{x2}\left( {s,\tau } \right) = \mathop {\smallint }\nolimits_{ - \infty }^\infty x1\left( t \right)\frac{1}{{\sqrt s }} \,*\, \left( {\frac{{t -\tau }}{s}} \right){\rm {d}}t$$
(3)

where * is the conjugate of the complex number, τ and s are the translation and dilation parameters responsible for amplitude and phase dynamics in time–frequency space; whilst ψ is the mother Morlet wavelet defined as

$$\psi \left( t \right) = \pi ^{ - \frac{1}{4}}\exp \left( {i\omega t} \right){{{\mathrm{exp}}}}\left( { - \frac{1}{2}t^2} \right),$$
(4)

where ω0 is set at 2π to ensure optimal joint time–frequency resolution compared to other types of wavelet functions such as the Mexican Hat, Paul and Gabor wavelets. In other words, the Morlet wavlets are most efficient in dealing with the Heisenberg uncertainty principle which insinuates that a trade-off between time and frequency i.e. when one is more precise, the other becomes less precise. Secondly, we extract the wavelet power spectrum (WPS) of the y(t) and (x(t) series (i.e. Wxx = |Wx|2 and Wyy = |Wy|2) as well as their cross-wavelet power spectrum (CWPS) (WPS)xy = Wxy = |Wxy|, from which the wavelet coherence, is computed as

$$R_{y.x1}\left( s \right) = \frac{{| {S( {W_{x1,y}} )} |}}{{[ {( {S\left| {W_{x1}} \right|^2} )( {S| {W_y} |^2} )} ]^{\frac{1}{2}}}}$$
(5)
$$R_{y.x2}\left( s \right) = \frac{{| {S( {W_{x2,y}} )} |}}{{[ {( {S\left| {W_{x2}} \right|^2} )( {S| {W_y} |^2} )} ]^{\frac{1}{2}}}}$$
(6)
$$R_{x1.x2}\left( s \right) = \frac{{| {S( {W_{x1,x2}} )} |}}{{[ {( {S\left| {W_{x1}} \right|^2} )( {S\left| {W_{x2}} \right|^2} )} ]^{\frac{1}{2}}}}$$
(7)

where S is a smoothing operator in both time and scale. The phase-difference dynamics are determined as

$$\phi _{x,y} = {\rm {Arctan}}^{ - 1}\left. {\left( {\frac{{I\{ {W_{xy}} \}}}{{\{ {{\it{\Re }}W_{xy}} \}}}} \right)} \right).$$
(8)

where π < ϕx,y < −π and provides information on (i) whether the pair of series are in-phase (positive) or antiphase (negative) synchronized and (ii) whether x leads y or vice-versa. Thirdly, we extend the wavelet coherence framework to control for other variables hence transforming the bivariate time–frequency analysis into a multivariate case. To this end, we compute the following partial wavelet coherence coefficients:

$$R_{y.x1\left| {x2} \right.}\left( s \right) = \frac{{| {R_{y.x1} - R_{y.x2} \,*\, R_{y.x1}} |^2}}{{[ {1 - R_{y.x2}} ]^2\left[ {1 - R_{x1.x2}} \right]^2}}$$
(9)
$$R_{y.x2\left| {x1} \right.}\left( s \right) = \frac{{| {R_{y.x2} - R_{y.x1} \,*\, R_{y.x2}} |^2}}{{[ {1 - R_{y.x1}} ]^2\left[ {1 - R_{x1.x2}} \right]^2}}$$
(10)

Data description

Our study uses three-time series in the empirical analysis collected on an annual frequency between 1960 and 2020. We source total fisheries production (in metric tons) and per capita GDP (gdppc) from the World Bank Development Indicators whilst the global land and ocean temperatures (GLAOT) are sourced from the National Oceanic and Atmospheric Administration (NOAA) database.

Table 1 presents the descriptive statistics of the series, and as can be observed the average per capita GDP of $5156 is observed which is in line with the World Bank’s classification of a middle-income economy and the relatively low standard deviation of $648 indicates that the values do not fluctuate too far from their averages. Moreover, the low skewness and kurtosis values indicate a slightly positive skewed series with no fat tails whilst the J–B statistic confirms that the data is normally distributed. For the temperature data, we note averages of 0.38 with a high standard deviation of 0.31 implying the series is very volatile even though the skewness, kurtosis, and J–B statistics indicate that the variable is normally distributed. Finally, the average Fisheries catch is 903,872 with a standard deviation of 375,364 (very volatile series) and is slightly positively skewed with fat tails which makes the series non-normally distributed.

Table 1 Descriptive statistics.
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Table 2 presents the correlation matrix amongst the variables from which we observe (i) a negative co-relationship between Fisheries and temperatures, (ii) a negative co-relationship between Fisheries and per capita GDP, and (iii) a positive co-relationship between temperatures and per capita GDP. However, note that these preliminary correlations do account for time and/or cyclical variation which we further consider in our main empirical analysis.

Table 2 Correlation matrix.
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Empirical results

WPS

We begin by presenting the WPS plots for all variables alongside their time series plots in Figs. 1–3. Whilst the time series plots show the evolution of the strength of a signal along a time domain, the WPS depicts the distribution of energy at different time intervals and scales for a given time series. The horizontal axis on the WPS denotes time, while the vertical axis represents the inverse proportionality of frequency variations with respect to time (f = 1/t). Higher frequencies correspond to shorter and more abrupt cycle periods, whereas lower frequencies correspond to longer and smoother cycles. The contour colours indicate the strength of variation at different cycles, with warmer colours representing more robust variation and cooler colours indicating weaker variation. The white lines surrounding the coloured contours represent the 5% significance level, calculated using 1000 simulation runs.

Fig. 1: Time series and WPS plot for fisheries.
figure 1

a Time series plot and b wavelet power spectrum.

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Fig. 2: Time series and wavelet power spectrum plot for global land and ocean temperatures in oC.
figure 2

a Time series and b Wavelet power spectrum.

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Fig. 3: Time series and wavelet power spectrum plot for per capita GDP in US$.
figure 3

a Time series and b wavelet power spectrum.

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The time series show that whilst Fisheries have been on a downward trend over the last couple of decades, global temperatures have been on an upward course whereas economic development has had an ‘N-shaped’ trajectory with two peaks in 1981 and 2019 and a trough in 1992, with the latter corresponding to a pre-democratic period when the nation had reached heightened political tensions, the economy was falling under the pressure of international economic sanctions and the country was experiencing severe drought (Rodrick, 2008; Ibebuchi, 2021). The WPS for all time series indicate dominant frequency bands between 64 and 128 years cycles hence unveiling long-run cyclical patterns in the series. Note that the long-run frequencies are more pronounced for gdppc variable (Fig. 3) which produces stronger cyclical variation (red colour) between 1980 and 2015, with cyclicity being strongest in the mid-1990s, which captures the downswing in the pre-democratic period of 1980–1994 and the upswing in a post-democratic period of 1995–2015. Moreover, the WPS plots are able to capture bouts of short-run frequencies for (i) Fisheries between 1965–1975 (4–8 year cycles) and 1985–1990 (4–8-year cycles) corresponding to pre-democratic periods when the Fisheries sectors were not well managed and Fisheries catches experienced cycles of sharp increases followed by sharp decreases (sign of overfishing), and (ii) temperatures between 2008 and 2020 (8–32 year cycles) corresponding to periods when temperatures almost doubled from 0.55 to 1.01 °C as well as between 2016 and 2020 (2–4-year cycles) corresponding to the more recent decreases in temperatures from 1.03 °C in 2016 to 0.98 °C in 2019.

Wavelet coherence analysis

We now present the wavelet coherence plots which present a spectral visualization of the time–frequency co-movement plane between (i) per capita GDP and Fisheries catch (Fig. 4), (ii) GLAOT and Fisheries catch (Fig. 5) and (iii) GLAOT and per capita GDP (Fig. 6) and the time-series plots of these co-movements are also provided for comparison sake. The colour contours in the wavelet coherence plots represent the strength of synchronization between the variables, while the faint white lines surrounding them indicate the 5% significance level achieved through 1000 simulation runs.

Fig. 4: Time series plot and wavelet coherence between per capita gdp and fisheries.
figure 4

a Time series plot and b wavelet coherence spectrum.

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Fig. 5: Time series plot and wavelet coherence between global land and ocean temperatures and fisheries.
figure 5

a Time series plot and b wavelet coherence spectrum.

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Fig. 6: Time series plot and wavelet coherence between global land and ocean temperatures and per capita GDP.
figure 6

a Time series plot and b wavelet coherence spectrum.

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The arrows inside the spectrum plots indicate the phase dynamics between the series and offer information on the direction of causality (i.e., whether ‘variable x’ leads ‘variable y’ or vice versa) and the ‘sign of the relationship’ (positive or negative). If the variables have positive (negative) correlations, the arrows’ notations are ↑, ↗, → and ↘ (↓, ↙, ←, and ↖), indicating that the series are in-phase (anti-phase). Furthermore, the arrows orientations specify whether ‘variable x’ leads (lags) ‘variable y’, with the arrows ↑, ↗, and → (↘) indicating that the series are in-phase, with ‘variable x’ leading (lagging) ‘variable y’, while the arrows ↓, ↙, and ← (↖) indicate that the series is anti-phase, with ‘variable x’ leading (lagging) ‘variable y’.

Figure 4 presents wavelet plots indicating positive co-movement or in-phase dynamics between per capita GDP and Fisheries in South Africa. These co-movements are observed across two frequency bands: 8–16 years and 16–40 years cycles, extending over the entire time period. The results suggest a positive relationship between economic development and Fisheries catches in South Africa, which is contrary to the U-shaped curve found in Madhoo (2011) and Peng et al. (2020) or the inverted U-shaped curve found in Peng et al. (2020) and Rashdan et al. (2021). Therefore, South Africa follows a developmental growth path, wherein the economic structure depends on agriculture and large-scale fishing, and higher development does not lead to a reduction in Fisheries captures. This may be attributed to the stringent regulatory laws and Fisheries management practices implemented since the 2000s, as noted in Ortega-Cisneros et al. (2021).

The wavelet plot presented in Fig. 5 between temperatures and Fisheries catches reveals the presence of nonlinear phase dynamics in the evolution of frequency components over time. Specifically, a dominant low-frequency component with cycles of 15–30 years is observed, showing anti-phase dynamics (negative co-movement) from 1960 to 2000, followed by in-phase dynamics (positive co-movements) from 2000 to 2020. Notably, this frequency band extends across the entire time period, indicating that temperatures lead to Fisheries catches. Secondly, a higher frequency component with cycles of 8–10 years is observed, extending from 1960 to 2000, wherein Fisheries lead GLAOT with anti-phase dynamics (negative co-movement) from 1960 to 1985, and in-phase dynamics (positive co-movement) from 1985 to 2000. Collectively, these findings suggest a U-shaped relationship between Fisheries and temperatures over the long- and medium-run, with a turning point around 2000. This implies that the Fisheries sector in South Africa has become increasingly resilient to temperature changes over the last two decades.

Figure 6 presents a wavelet coherence plot between temperatures and per capita GDP, revealing the presence of nonlinear phase dynamics at low-frequency cycles of 20–40 years. Initially, in-phase dynamics (positive relationship) between 1960 and 2000 are observed, which subsequently change to anti-phase dynamics (negative relationship) in the post-2000 era. These results imply an inverted U-shaped relationship, also referred to as a ‘humped’ relationship, between temperatures and GDP, which has been established in previous empirical studies (Dell et al., 2012; Li et al., 2019; Chang et al., 2020; Kalkuhl and Wenz, 2020). Notably, Li et al. (2019) and Chang et al. (2020) suggest that warmer temperatures can initially assist economic development by increasing agricultural yields and boosting tourism, but may hinder growth at higher temperatures through reduced worker productivity, higher energy costs, and damages to infrastructure and crops.

Partial wavelet coherence analysis

We now present our findings from the partial wavelet coherence analysis. To recall, we model two multivariate time–frequency functions, one describing the coherence between fisheries and per capita GDP whilst controlling for the effects of temperatures (Fig. 7) and another for the coherence between fisheries and temperatures whilst controlling for per capita GDP (Fig. 8). The results show weaker coherence between per capita GDP and Fisheries once temperatures are controlled for and significant coherence effects only occur in the post-2010 period at the frequency band of 6 years. Conversely, we observe that the phase dynamics between temperatures and Fisheries do not change much after controlling for per capita GDP.

Fig. 7
figure 7

Partial wavelet coherence spectrum between per capita GDP and fisheries controlling for global land and ocean temperatures.

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Fig. 8
figure 8

Partial wavelet coherence spectrum between global land and ocean temperatures and fisheries controlling for per capita GDP.

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Overall, our PWC analysis suggests that the U-shaped (inverted U-shaped) relationship between temperatures and fisheries catches (economic development) remains unaltered when controlling for economic development (fisheries stock), whereas the GDP-fisheries relationship turns largely insignificant when controlling for temperatures.

Conclusions

The present study investigated the impact of economic development and global temperature fluctuations on Fisheries catches in South Africa spanning from 1960 to 2021 through a three-staged continuous wavelet analysis. The first stage employed the WPS to explore the time–frequency evolution of the time series, revealing a long-run downward trend in Fisheries catches with more cyclical volatility before 1995 that has since been smoothed out. The second stage conducted wavelet coherence analysis to investigate the time–frequency co-movements between climate change, economic development, and Fisheries catches. The analysis revealed a U-shaped temperatures–Fisheries relationship, a positive economic development–Fisheries relationship, and a ‘humped’-shaped temperatures–economic development relationship. Lastly, partial wavelet coherence analysis was employed to further investigate the time-frequency relationship between climate change and Fisheries catches whilst controlling for economic development, and the economic development and Fisheries catches whilst controlling for climate change, revealing that the former relationship remains unchanged while the latter relationship loses much of its long-run significance.

Overall, the analysis provides a comprehensive picture of the (co)-evolution of climate change, economic development, and Fisheries catches in South Africa over the past five decades. For starters, the WPS analysis revealed that while Fisheries exhibited a long-term downward trend, the series displayed greater cyclical volatility before 1995, which was subsequently smoothed out in the post-democratic era. Similarly, temperatures and economic development displayed upward long-term trends, albeit with strong short-term cyclical fluctuations. Specifically, economic development experienced fluctuations during the pre-democratic era due to severe economic and political distress, whereas global temperatures displayed sharp increases between 2011 and 2016 and again between 2018 and 2021 when global efforts to lower temperatures began to materialize. The wavelet coherence analysis further indicated that the long-term upward trend in economic development during the post-democratic era was associated with steady increases in Fisheries catches, with no significant signs of overfishing. The long-term adverse impact of temperatures on Fisheries was mitigated following the implementation of policies such as the quota-based Policy for the Allocation and Management of Fishing Rights, Small-Scale Fisheries Policy, and electronic monitoring measures introduced between 2008 and 2014. However, the same policies did not prevent temperatures from adversely impacting economic development during the sharp temperature increase period post-2010, following the global recession. Finally, the partial wavelet analysis indicated that although Fisheries have become more resilient to climate change, the positive impact of improved economic development on Fisheries is overshadowed by high temperatures.

Based on our analysis, we conclude that the South African Fisheries sector has exhibited increased resilience to the challenges of economic development and climate change over time. This outcome can be primarily attributed to the implementation of robust domestic policies, including the Small Scale Fisheries Policies, electronic monitoring measures, and the substantial expansion of Marine Protected Areas observed between 2019 and 2020. Such progress holds valuable lessons for other African countries that are heavily reliant on Fisheries markets yet lack adequate freshwater ecosystem protection policies to prevent overfishing practices.

Furthermore, our findings indicate that while temperatures have exhibited a decreasing trend in recent years of 2016–2018, global efforts to mitigate climate change have not been extensive enough to induce a long-term downward trend in this variable. Therefore, there exists an urgent need for global policymakers to take action towards inducing a substantial reduction in global temperatures over the coming decades.

Our research also demonstrates that while Fisheries catches may exhibit greater resilience to climate change, per capita income is vulnerable to rising temperatures, which could ultimately impact the future trajectory of Fisheries catches. Thus, policymakers in South Africa must develop comprehensive policy frameworks that do not treat the resilience of Fisheries and per capita income to climate change as mutually exclusive objectives.

One noteworthy limitation of our study is the manner in which we have treated Fisheries catches as a composite measure that incorporates both aquaculture and wild-capture fisheries. Given that aquaculture fisheries constitute a smaller yet more environmentally sustainable sub-component of the overall Fisheries catches, it would be informative to explore the resilience of the aquaculture sector to climate change. As such, it could be beneficial for forthcoming research to investigate this as a possible avenue for future inquiry. Additionally, it would be fruitful for future studies to expand their scope to other African economies, which are susceptible to conflicts that arise from inadequate fisheries management practices.