Renewable Energy Firms and Green Transition: Economic and Social Impacts

We aim to examine how renewable energy adoption influences socio-economic outcomes in Saudi Arabia, a hydrocarbon-dependent economy actively pursuing ambitious energy transition reforms. Focusing on three key dimensions—labor market dynamics, energy access, and GDP growth—we assess the interplay between renewable energy initiatives and their broader socio-economic implications. The study builds on methodologies from Blundell and Bond [21], which link renewable investments to labor markets and equity outcomes while addressing gaps in firm-level analysis specific to hydrocarbon-rich contexts. The selected variables reflect localized impacts (decentralized energy access) and macroeconomic linkages (e.g., growth via innovation spillovers), providing a framework to assess how Saudi Arabia’s renewable transition aligns with Vision 2030’s sustainability and economic diversification goals.

The dependent variables—EMP, LEA, and GDP—are chosen based on their demonstrated relevance in energy transition literature. Employment gains from renewable projects are well-documented, with Jenkins et al. [22] showing that solar and wind investments create more jobs per unit of energy than fossil fuels. Meanwhile, Sovacool [23] argued that GDP growth in oil-dependent economies benefits from renewable diversification, as it reduces exposure to commodity price volatility. The Level of Energy Access accurately reflects the actual situation in Saudi Arabia by capturing the percentage of households and firms with renewable energy access, particularly through decentralized systems supported by Vision 2030 reforms. However, we acknowledge some challenges in obtaining this data, including limited granularity at the household level and reliance on firm and government reports, which may not fully capture informal or off-grid access patterns. The explanatory variables—RII, renewable energy share (RE), and R&D—are grounded in firm-level evidence. Our study defines RII as a firm’s capital expenditure (CAPEX) specifically allocated to planning, developing, constructing, and integrating renewable energy assets. This includes investments in both centralized utility-scale projects and decentralized systems enhancing energy access, such as solar farms, wind turbines, hydrogen fuel systems, battery storage facilities, and microgrids. RII encompasses not only the physical deployment of these assets but also the associated enabling infrastructure, like grid interconnections and digital monitoring systems. Crucially, RII is quantified as the log ratio of this specific CAPEX to firm revenue. This definition ensures RII provides a comprehensive, holistic measure of renewable infrastructure activity within firm-level operations. Control variables account for contextual factors. Howitt and Aghion [24] highlighted oil price volatility (OILV) as a critical disruptor of energy transitions. Firm-specific controls (size, age, debt) show that organizational characteristics mediate green investment outcomes. Descriptions of all variables included in our study are reported in Table 1.

Table 1. Variables description

Table 2. Summary statistics

Table 3. Correlations matrix

RE shows meaningful correlations with employment (EMP: 0.45) and local energy access (LEA: 0.62), supporting research demonstrating how clean energy deployment creates jobs and reduces energy poverty. Infrastructure investment similarly correlates with employment (0.38) and energy access (0.51), aligning with findings that capital-intensive renewable projects generate higher-quality jobs than temporary fossil fuel work. The powerful connection between Vision 2030 progress (V2030) and RE (0.63) reflects studies showing that policy consistency is crucial for overcoming transition barriers. GDP growth shows its strongest renewable energy correlation with RE (0.39), consistent with evidence that clean energy adoption contributes to economic stability. The negative correlations between oil price volatility and socio-economic outcomes (GDP: -0.33, EMP: -0.25) reinforce research on fossil fuel market instability's developmental costs, while renewable energy variables demonstrate more resilient positive associations. These patterns, with correlation magnitudes typically ranging between 0.30-0.60, suggest renewable energy adoption can deliver simultaneous benefits across employment, energy access and economic growth when supported by targeted policies and investments - a finding consistent with but extending prior national-level studies through its firm-level perspective.

The chart in Figure 1 demonstrates how RII (blue, left axis) rose from about -2.5 to -0.8, with a noticeable acceleration after 2016. The EMP (green, right axis) follows a similar upward trend, growing from 58% to 75%. The vertical dashed line at 2016 marks the Vision 2030 policy inflection, after which both variables show steeper growth. This visualizes the strengthening correlation between infrastructure investment and employment, especially as labor market reforms and renewable projects scale up.

Figure 1. Renewable infrastructure investment (RII) vs. employment rate (EMP)

In Figure 2, the purple line (left axis) shows the share of renewables in the energy mix, climbing from 2% to 28%, with a much faster increase after 2016. The orange line (right axis) tracks local energy access, rising from 15% to 65%. The post-2016 period features a tighter coupling between these variables, reflecting how decentralized renewable projects under Vision 2030 have expanded access, especially in underserved areas. The dashed line in 2016 again highlights the policy-driven inflection point.

Figure 2. Renewable energy share (RE) vs. local energy access (LEA)

Figure 3. R&D spending vs. GDP growth

The results reveal distinct patterns across the three dependent variables, with renewable energy drivers exerting varying magnitudes and mechanisms of influence on socio-economic outcomes.

5.1 Panel data analysis

Results presented in Table 4, which analyzes EMP as the dependent variable, reveal that RII exhibits the strongest association, with coefficients ranging from 0.102 to 0.173 across model specifications.

A 1-unit increase in RII correlates with a 0.17 percentage point rise in EMP in baseline models. The interaction term RII × RE (0.215) in Column 3 suggests that combining infrastructure deployment with renewable energy adoption amplifies employment effects, supporting the argument that technological complementarities drive labor demand. However, the weaker significance of RE alone (0.121 in Column 1 vs. -0.065 in Column 4) implies renewable adoption alone may not suffice in sectors entrenched in fossil fuel dependencies. Vision 2030 progress (0.201) and subsidies (0.118) further reinforce employment gains.

Analysis of LEA (as the dependent variable) in Table 5 reveals that RII exhibits larger coefficients (0.132 to 0.214) than those in EMP models, highlighting its direct role in expanding energy infrastructure.

Table 4. Impact on employment rate (EMP)

Note: Table 4 presents regression results using four specifications, where estimates refer to the equation with Employment Rate as the dependent variable. Column (1) uses System GMM with baseline controls, Column (2) applies Difference GMM for robustness, Column (3) includes interaction terms between renewable drivers, and Column (4) focuses on a subsample of energy-intensive firms. The significance levels are denoted by *, **, and *** at 10%, 5%, and 1%, respectively, and standard errors are provided in parentheses.

A 0.21-unit increase in LEA per RII unit underscores the immediate benefits of decentralized systems. The RE coefficient (0.165 in Column 1) highlights the additive effect of renewable adoption, but its insignificance in energy-intensive subsamples (-0.088) signals persistent access disparities in fossil-reliant regions. Based on the study’s findings, fossil-fuel-dependent regions face specific structural challenges in renewable transitions: entrenched labor market rigidities that hinder workforce reallocation (evidenced by persistent employment responsiveness gaps), grid inertia limiting decentralized renewable integration (reflected in LEA coefficient insignificance for energy-intensive subsamples in Table 5), and economic lock-in effects that mute GDP responsiveness to renewable investments (Table 6). Policy measures should address these through sequenced interventions: 1/Early phase (Years 0–4): Targeted infrastructure subsidies and retraining programs to overcome labor-market rigidities and grid constraints, prioritizing decentralized energy access; 2/ Medium phase (Years 4–6): Countercyclical funding during oil price troughs (e.g., sovereign wealth allocations) to amplify GDP impacts, coupled with SME-focused incentives to mitigate scale inequities; 3/ Cross-cutting: Spatially tailored implementation of Vision 2030, embedding equity safeguards like local job quotas to prevent regional exclusion.

Table 5. Impact on local energy accessibility (LEA)

Note: Table 5 presents regression results for Local Energy Accessibility (LEA) as the dependent variable, using four specifications. Column (1) uses System GMM with baseline controls, Column (2) applies Difference GMM for robustness, Column (3) includes interaction terms between renewable drivers, and Column (4) focuses on a subsample of energy-intensive firms. The significance levels are denoted by *, **, and *** at 10%, 5%, and 1%, respectively, with standard errors in parentheses.

The RII×RE interaction (0.258) again underscores synergies, where infrastructure and adoption jointly reduce energy poverty. Subsidies (0.153) and Vision 2030 (0.236) show more substantial impacts here than in EMP models, suggesting policy instruments disproportionately enhance access when targeting marginalized groups.

When the GDP Growth Rate is modeled as the dependent variable (Table 6), renewable drivers exhibit attenuated effects, with RII coefficients peaking at 0.092.—less than half the magnitude seen in LEA models.

The RE coefficient (0.067) further diminishes in robustness checks (0.041 in Column 2), implying that GDP contributions of renewable adoption are sensitive to external factors like oil prices (-0.128 in Column 4). Vision 2030 retains significance (0.158), but its smaller coefficient than EMP/LEA models suggests GDP growth depends on broader economic reforms beyond renewable policy alone. Interaction terms (e.g., RII×RE at 0.162) show limited significance, reinforcing that GDP benefits require cross-sectoral maturation.

Furthermore, adding quadratic terms for RII, RE, and R&D yielded statistically insignificant coefficients (p > 0.10) without materially improving model fit (adjusted R² changes < 0.01). These findings, combined with the robust linear relationships observed in our baseline and stratified analyses (Table 7) and the stability of coefficients under alternative variable constructions, confirm that linear specifications adequately capture the core relationships. While interaction terms (e.g., RII × RE) effectively model synergistic non-additive effects, the absence of significant curvilinear patterns suggests our primary linear models are appropriate and that the reported marginal effects provide reliable estimates of renewable energy impacts.

Table 6. Impact on GDP growth rate

Note: Table 6 presents regression results for GDP Growth Rate as the dependent variable, using four specifications. Column (1) applies System GMM with baseline controls, Column (2) uses Difference GMM for robustness, Column (3) includes interaction terms between renewable drivers, and Column (4) focuses on a subsample of energy-intensive firms. The significance levels are denoted by *, **, and *** at 10%, 5%, and 1%, respectively, with standard errors in parentheses.

LEA presents perhaps the most direct and measurable benefits from renewable energy interventions, with a powerful performance in the latter study period. The 0.227 coefficient for RII during 2017-2024 significantly outperforms the 0.158 observed in earlier years, demonstrating how policy focus can dramatically improve energy access outcomes. This acceleration likely reflects technological advancements and enhanced implementation frameworks under Vision 2030. The role of government subsidies (GOV) emerges as particularly crucial for LEA, with a 0.194 impact in the later period, underscoring how financial incentives can effectively bridge the gap for underserved populations. However, the analysis reveals important temporal nuances - while standalone renewable adoption (RE) shows respectable performance (0.191 post-2016), its impact is substantially amplified when combined with infrastructure development, as evidenced by the powerful 0.274 interaction effect. These findings strongly support integrated approaches to energy access that combine multiple renewable interventions rather than relying on single solutions.

The economic growth rate tells a more complex story about the macroeconomic impacts of renewable energy. While positive relationships exist, the effects are generally more modest and gradual than employment and access indicators. Though statistically significant, the 0.094 coefficient for RII in the post-2016 period suggests that infrastructure investments alone may not be sufficient to drive substantial GDP growth in the short to medium term. This aligns with broader economic theory about the time required for structural transformation in energy systems. The analysis reveals an interesting dichotomy in how renewable measures interact with oil market conditions - during periods of low oil prices (≤ $75/bbl), renewable investments show stronger GDP correlations (0.077 for RII) compared to high-price environments (0.031). This pattern may reflect both the increased competitiveness of renewables when fossil fuels are expensive and the greater fiscal space for energy transition investments during commodity booms. Compared to more robust employment and access synergies, the relatively muted interaction effects for GDP (RII×RE at 0.135) suggest that economic growth benefits from renewables may require more comprehensive policy packages beyond the energy sector itself.

GDP growth's responsiveness to renewable investments is acutely moderated by oil prices, as evidenced by RII's GDP coefficient doubling during low oil price periods (0.077 vs. 0.031 in high-price regimes (Table 7)). This pattern reflects several interrelated dynamics: first, low oil prices constrain fossil fuel revenues, expanding fiscal space for renewable diversification as a countercyclical strategy—such as allocations from the Saudi sovereign wealth fund. Second, reduced fossil fuel subsidies under low-price conditions enhance the competitiveness of renewables, improving grid parity and attracting greater private investment. Third, on the demand side, oil price troughs increase household and industrial energy burdens, spurring demand for decentralized, cost-effective renewable solutions like solar microgrids, which in turn amplify GDP spillovers. In contrast, periods of high oil prices tend to crowd out renewable investments due to renewed fossil fuel rent-seeking, thereby delaying macroeconomic growth impacts even when employment and energy access gains remain visible.

Table 7. Socio-economic impacts of renewable energy drivers: system GMM estimator results

Note: Table 7 represents the results of different specifications of our examination. Columns represent distinct subsamples: 2012–2016 (pre-Vision 2030), 2017–2024 (post-Vision 2030), High Oil (oil price > \$75/bbl), Low Oil (oil price ≤ \$75/bbl), Large Firms (assets > 1B), and Small and Medium-sized Enterprises (SMEs) (assets ≤ 1B). All models are estimated using System GMM with firm fixed effects. Standard errors are reported in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

The temporal analysis provides compelling evidence for policy effectiveness, with all three socio-economic indicators showing marked improvement following Vision 2030's implementation. Policy impacts are particularly evident in the employment sector, where the policy period coefficients are 56% higher on average for key renewable variables compared to the pre-policy era. Acceleration is even more pronounced for energy access, with post-2016 coefficients nearly doubling in some specifications. Findings support the hypothesis that coherent national strategies can serve as powerful accelerators for energy transition benefits. However, GDP results introduce an important caveat—while renewable measures contribute to economic growth, their effects are more gradual and contingent on broader economic conditions than direct social indicators like jobs and energy access. These patterns suggest that while renewables can form a crucial component of development strategies, they may need complementary investments in human capital, institutional reform, and economic diversification to realize their growth potential fully. The analysis ultimately paints a picture of renewable energy as a powerful but nuanced tool for socio-economic development, with effects varying significantly across outcome metrics and implementation contexts.

5.2 IRFs visualizations

To complement our panel data regressions—which revealed static relationships between renewable energy drivers and socio-economic outcomes—we employ IRFs to analyze the dynamic evolution of these impacts over time. Simulating a one standard deviation shock to RII and Renewable Energy Adoption (RE), we track responses in employment (EMP), energy access (LEA), and GDP growth across an 8-year horizon. This approach quantifies effects' magnitude, persistence, and decay, with 90% confidence intervals reflecting estimation uncertainty.

Visualized through two figures (RII in blue, RE in red), subplots for each outcome variable (EMP, LEA, GDP) display deviations from baseline on the y-axis against years 0–7 on the x-axis. Solid lines represent mean responses; shaded regions denote confidence bounds, and vertical dashed lines at years 4–6 highlight medium-term effects, annotated with precise values. By bridging short-term policy actions (e.g., Vision 2030 infrastructure initiatives) with long-term trajectories, this analysis provides policymakers with a granular understanding of how renewable transitions generate socio-economic dividends across varying timeframes.

For RII shocks in Figure 4, EMP responds rapidly, peaking at year 3 before gradually declining, reflecting strong but transient job creation from infrastructure projects. LEA exhibits slower momentum, peaking at year 4 and decaying gently, suggesting sustained benefits from expanded renewable infrastructure. GDP growth, however, rises quickly but fades faster, indicating that macroeconomic gains from infrastructure investments are front-loaded and less persistent.

The temporal variation in RII's employment impact aligns with renewable project investment cycles. As shown in Table 7, RII's coefficient for employment surges from 0.121 (pre-2016) to 0.189 (post-2016), reflecting Vision 2030's policy acceleration. This corresponds to the typical 3–5-year investment cycle for renewable infrastructure (e.g., solar/wind project development). The IRF analysis (Figure 4) further validates this: employment peaks at Year 3 post-RII shock—coinciding with the construction/installation phase when labor demand peaks. Beyond Year 3, employment effects decay as projects transition to operational phases requiring fewer workers. Large firms exhibit stronger cyclical responsiveness (0.177 vs. SMEs’ 0.098) due to economies of scale in synchronizing capital deployment with labor mobilization.

In contrast, RE shocks in Figure 5 generate more gradual employment effects, peaking at year 4 and declining slowly, consistent with the phased adoption of renewable technologies. LEA shows the largest and most persistent response to RE shocks, peaking at year 5 and maintaining near-peak levels, underscoring renewable adoption’s long-term role in democratizing energy access. GDP growth under RE shocks remains modest, peaking early and fading steadily, mirroring the transient growth patterns observed with RII.

These results reveal critical nuances: RII shocks prioritize short-term employment and GDP gains, while RE shocks drive sustained energy access improvements and gradual job creation. The disparity in persistence—infrastructure-driven employment fading faster than adoption-driven energy access—highlights the need for policy frameworks that balance immediate economic stimuli with long-term equity goals. The confidence intervals, widest during initial years for GDP and narrowing over time for LEA, underscore the reliability of energy access outcomes compared to growth metrics. Annotations at years 4–6 further quantify these dynamics, showing, for example, that LEA under RE shocks retains 85% of its peak effect by year 6, compared to just 45% for EMP under RII.

Figure 4. Dynamic effects of renewable infrastructure investment (RII) shocks on employment, energy access, and GDP

Figure 5. Sustained Impacts of renewable energy adoption (RE) shocks on socio-economic outcomes