Innovational duality and sustainable development: finding optima amidst socio-ecological policy trade-off in post-COVID-19 era

Purpose – This study aims to analyze the socio-ecological policy trade-off caused by technological innovations in the post-COVID-19 era. The study outcomes are utilized to design a comprehensive policy framework for attaining sustainable development goals (SDGs). Design/methodology/approach – Study is done for 100 countries over 1991 – 2019. Second-generation estimation method is used. Innovation is measured by total factor productivity, environmental quality is measured by carbon dioxide (CO 2 ) emissions and social dimension is captured by unemployment. Findings – Innovation – CO 2 emissions association is found to be inverted U -shaped and innovation – unemployment association is found to be U -shaped. Research limitations/implications – The study outcomes show the conflicting impact of technological innovation leading to policy trade-off. This dual impact of innovation is considered during policy recommendation. Practical implications – The policy framework recommended in the study shows a way to address the objectives of SDG 8, 9 and 13 during post-COVID-19 period. Social implications – Policy recommendations in the study show a way to internalize the negative social externality exerted by innovation. Originality/value – This study contributes to the literature by considering the policy trade-off caused by innovation and recommending an SDG-oriented policy framework for the post-COVID-19 era.


Introduction
Technological innovations, specifically digital innovations, are expected to generate higher amount of value and resources for the economy in the coming years (Unalan & Ozcan, 2020;Jha & Bose, 2015). Recent global disruptions like COVID-19 have exacerbated the need and the push towards higher levels of digitalization as well (Gurbuz & Ozkan, 2020a). The role of such innovations in leading the world towards a more prosperous future cannot be understated.
However, technological innovations have not always yielded equitable outcomes for all sections of society (Van der Waal et al., 2021). Further, the ongoing COVID-19 Pandemic has increased the inequalities that were initiated due to unequal distribution of benefits between different sections of society (Swinnen, 2020). This underlines the importance of policies that can bring about a more sustainable and equitable development.
The United Nations 2030 agenda for sustainable development comprising 17 sustainable development goals (SDG) is one of the indicators that nations across the world have adopted to achieve sustainable development. The ongoing COVID-19 Pandemic poses a serious question mark on attaining the SDGs by 2030. Mukarram (2020) has identified the dire impact of COVID-19 pandemic on nations' ability to meet their SDGs. In this backdrop, technological innovation can emerge as an important factor.to meet SDGs in the post-COVID world. For instance, the change in work style, i.e., from the conventional office set up to remote working, highlights the importance of automation and digitization. It highlights the crucial role of technological innovations in attaining SDGs related to the livelihood of people. Hence, it can be concluded that technological innovation can be an instrumental factor in achieving SDGs in the post-COVID world. Recently scholars have argued for increased usage of digital media to raise awareness for SDGs (Grover et al. 2021), usage of technological innovations to hasten meeting sustainability goals (Girbuz & Ozkan 2020b). Gupta et al. (2021) in a recent call for papers for this special issue highlighted the need for technological innovations to achieve SDGs in post-pandemic world. In this study, we look at the role and the limiting effects of technological innovations in meeting SDGs.
While the above discourse points to the utility of technological innovation in enabling societies to meet their SDGs, technological innovations also have a dark side, especially with regards to social indicators such as employment. In this context, there exist two contradictory opinions. Scholars such as Greenan and Guellec (2000), Benavente and Lauterbach (2008), and so on opine that technological innovation can create new jobs. On the contrary, researchers such as Vivarelli (2015), Gagliardi (2019), and so on deduce that innovation often replaces manual labor with technology, thus raising unemployment. For instance, decline of coal mining leads to lower carbon footprint. This is on account of technological innovation due to higher reliance on hydrogen or solar energy as well as new mechanized mining technologies.
However, it also leads to higher unemployment in the manual labor-intensive industry. As the example shows, technological innovations may lead to more sustainable development but it is not always conducive to generating employment. Now, achieving sustainability entails the accomplishment of economic, environmental, and social dimensions of sustainability. Hence, the policy frameworks should maintain a balance between these three aspects. Whether an innovation is going to emerge as an environmental panacea, or it is going to open pandora's box of social imbalancesemerges as a major question in the post-COVID world. In summary, attaining the objectives of Agenda 2030 is largely dependent on the innovation capacities of nations, while these innovations might deter the developmental trajectory by pushing the nations towards a social imbalance. To manage this probable problem, looking beyond the obvious benefits of innovation becomes necessary.
The impact and implications of COVID-19 Pandemic is still raging around us and the implication of new policies have not yet been fully observed. In this study, we take a historical account of innovation strategies and their implications in terms of employment and meeting SDGs and attempt to create a policy framework for future. This study focuses on the following research question.
• Research Question: Will there be conflicting social and ecological impacts of technological innovation in attaining SDG objectives during post-COVID-19 period?
We hypothesize that in view of the innovation-led socio-ecological policy trade-off expected in the post-COVID-19 period, a policy reorientation is necessary. This policy reorientation might enable the nations to transform the prevailing economic growth trajectory and policy regimes towards the achievement of SDG objectives. In this pursuit, the present study empirically assesses the dual impact of innovation on CO2 emissions and unemployment for 100 countries over 1991-2019. The analysis is conducted across several income levels, for a broader impact assessment. Based on the outcomes, an SDG-oriented pro-developmental comprehensive policy framework has been recommended. This policy framework is designed in a way to internalize the negative social externalities of innovation, while enhancing its positive environmental externalities. This multipronged policy-driven approach to take account of the innovation-led policy trade-off has not been attempted in the academic literature, and there lies the policy-level contribution of the study. The need for such studies has also been highlighted by Horisch (2021) who call for policy research to enable meeting SDGs in post COVID-19 world. Now, in order to achieve this objective, a theoretical framework is required to capture the evolutionary impacts of the policy instruments. The framework should capture this impact across a group of countries. Moreover, the framework needs to take account of the associative nonlinearity among the model parameters. Hence, the Environmental Kuznets Curve (EKC) hypothesis has been adopted for the empirical analysis. Lastly, as the countries are associated with each other via economic spillovers, the estimation method needs to consider the cross-sectional dependence. Based on this assumption, second generation panel data estimation methods are used. This theoretical and methodological complementarity with the research objective sanction the analytical contribution of the study.

Literature review
The scholarly works related to this study can be classified into three works; scholarly works on Impact of technological innovation on Sustainable Development Goals (SDG) and CO2 emissions, impact of technological innovation on employment, and impact of COVID-19 Pandemic on Sustainable Development Goals (SDG).

Impact of technological innovation on SDGs and CO2 emissions
In recent times, there is a rising interest among scholars to explore the effect of technological innovation on SDGs. Table 1 contains a summary of some of the major studies in the domain.
While, Mensah et al. (2018) explain how the technological development of the OECD countries influences their CO2 emissions over 1990-2014, Modgil et al. (2020) propose a modern information decision support system for achieving the SDGs. Sinha et al. (2020) investigate the effect of technological innovations on environmental degradation, sustained economic growth, clean and affordable energy, and quality of education for the period 1990-2017. Bag et al. (2021) opine that big data analytics-based artificial intelligence can be an important factor for the operations of circular economy and achieving the SDGs. Chien et al. (2021) propose an information and communication technology (ICT)-based framework for the BRICS countries to combat environmental degradation and achieve the SDGs.
The exploration of the existing literature related to SDGs highlight the researchers focus on diverse topics such as performance indicators of food loss reduction (AL-Dalaeen et al., 2021), the influence of political leadership on the SDGs (Grover et al., 2021), information decision system-based framework design (Modgil et al., 2020), the impact of technological innovation on achieving SDGs , humanitarian decision making (Marić et al., 2021) etc.
However, the investigation into the effect of technological innovation attaining the SDGs and on the social indicator such as unemployment has not been paid enough attention. This is the gap our study aims to fill.

Impact of technological innovation on employment
Many researchers investigate the impact of technological progress on the employment of the economy. As per the analysis, there exist two contradictory opinions. A group of researchers opines that technological innovation raises the employment level of a nation. For instance, Greenan and Guellec (2000) investigate the impact of technological innovation on the employment opportunities for the French firms over 1986-90 and find the strong positive effect of innovation at firm-level as well as sectoral level. Similarly, Benavente and Lauterbach (2008) conclude that the technological innovation policy of Chile generated new jobs over 1998-2011. Another stream of research that has recently been of much interest to scholars studying industrial revolution and employment implications is 'Industry 4.0" (Koh et al., 2019;Xu et al., 2018). Industry 4.0 is also expected to significantly enhance firm productivity and employment in coming years (Ortt et al., 2020;Opazo-Basáez et al., 2021).
Another group of researchers believes that technological progress affects employment opportunities. Evangelista and Savona (2002) conduct an empirical study on the Italian firms

Impact of COVID-19 Pandemic on Sustainable Development Goals (SDG)
The impact of the ongoing COVID-19 pandemic on attaining SDGs has attracted the attention of researchers. There is a rising interest in analyzing how innovations can help nations and firms meet their SDGs. Thornton (2020) opines that the pandemic has a severe negative impact on attaining the SDGs. Pan and Zhang (2020) highlight the importance of in-depth investigation into the information systems to attain the SDGs in the post-COVID era. Modgil et al. (2021) identify the role of AI in meeting firms' resilience in post COVID-19 world.
However, our literature review finds lack of works that focus on the impact of technological innovation on achieving sustainable development goals and employment opportunities.
Specially, it emerges as a matter of concern for the nations in the post-COVID-19 era. In this context, an investigation into the dual socio-environmental effect of technological innovation for multiple countries over a longer period can facilitate efficient decision-making of policymakers. It acts as the main motivation of the study. To the best of knowledge, this is the first work that investigates the effect of technological innovations on SDGs and employment in COVID-19 pandemic era. The summary of the relevant literature and contribution of this work is presented in Table 1. It shows the gap that this study aims to fill as the first study with dataset from 100 countries analyzing impact of innovation on sustainability and employment.
None of the other studies in this field rely on dataset of this size and they also do not attempt to balance the impact of innovation on sustainability and employment.

Problem description
As the resistance to shift from the existing energy sources to renewable ones creates deterrence into the transition, environmental quality might not start improving immediately with the rise in technological innovation. Therefore, during the initial phases of innovation, environmental degradation might not start getting reduced. However, gradual diffusion of innovation and achieving economies of scale gradually bring down the cost of these solutions, leading to their rising acceptability. This can bring forth a steady decline in the growth of environmental degradation.
At the same time, the development and deployment of these solutions require skilled and unskilled labor, which eventually creates employment in the economy. Being capital-intensive, once these solutions are deployed, they start replacing human labor. Hence, the existing systems can be more efficient. Following Arrow et al. (1961), this replacement of labor with technology (capital) creates unemployment within the economy.
This dual impact of technological innovation might be catalyzed by the structural transformation of the economy, i.e., more service-orientation of the sectoral activities might increase unemployment, while environmental degradation might fall. On the contrary, manufacturing-oriented sectoral transformation might experience an opposite effect. Moreover, energy usage patterns might also have a moderating impact on the impact duality of innovation. As the energy-intensive production practices are more inclined towards being stemmed from the manufacturing sector, therefore higher usage of fossil fuel might have coexistence with higher employment. Moreover, the channel of international trade is also utilized to transfer the old and dirtier technologies to comparatively poorer and less developed countries for controlling the carbon footprint. Also, the transfer of greener technologies to host countries might boost innovation capabilities, which might have a consequence on environmental quality and unemployment. This impact of the globalization channel can be explained by the "Pollution Haven hypothesis" (Levinson and Taylor, 2008)

and the "Pollution
Halo hypothesis" (Antweiler et al., 2001). Lastly, the developmental trajectory of a nation needs to account for the balance between demand and supply of labor. Here, population growth can emerge as a crucial factor. If the population growth is higher than the demand of labor, the country is likely to suffer from the unemployment problem. Moreover, this incidence of unemployment might lead to income inequality and deterioration in living standards. This might add to the environmental degradation arising out of the unsustainable energy usage pattern.

Empirical model
Assessing the impact of technological innovation on environmental quality and unemployment entails considering a theoretical framework that can capture the evolutionary impact of technological innovation across a group of countries that may represent a wide set of innovation and enterprise level beliefs. Hence, the present study embarks on the Environmental Kuznets Curve (EKC) hypothesis framework. According to the seminal work of Grossman and Kruger (1991), environmental degradation starts rising during the early phase of economic growth. Once this economic growth reaches a threshold, improvement in the living standard raises environmental awareness among citizens. Henceforth, further growth in the economy leads to a decline in the environmental degradation. Quadratic specification of this hypothesis results in an inverted U-shaped association between environmental degradation and (drivers of) economic growth. This hypothesis is capable of revealing the nonlinear evolutionary impact of policy instruments on a target policy parameter for a pool of countries . The empirical schema of this association can be represented as follows: Now, given the two conflicting policy agendas, the shape of this association can take two different forms, based on the two dependent variables. This association is expected to follow a generally accepted inverted U-shaped form for CO2 emissions. Therefore, in this case, 1 > 0 and 2 < 0. On the other hand, the shape is expected to be U-shaped for unemployment. Hence, in this case, 1 < 0 and 2 > 0. For both the cases, the turnaround points of the association can be expressed as for Unemployment Now, in empirical pursuit, Eq. (1) can be explained as per the following: CE i,t = α 0 + α 1 TFP i,t + α 2 TFP 2 i,t + α 3 GLOB i,t + α 4 EU i,t + α 5 POP i,t + α 6 STR i,t The description of the dependent and independent variables presented in Eqs. (3) and (4) are expressed as follows: • CE= CO2 emissions, • UE= Unemployment, • TFP= Total Factor Productivity, • GLOB= Globalization, • POP= Population, • STR= Structural transformation of economy As the investment in research and development might not be realized fully in the innovation output, the output indicator of innovation can be helpful to capture the effects of innovation.
For this reason, in this study, TFP, an output indicator of innovation, is selected. STR is determined by the Lilien Index (Lilien, 1982), which measures the changes in labor share across primary, secondary, and tertiary sectors. It can be represented as follows: where, SE: Employment share in a particular sector, s = 1, 2, 3 : primary, secondary, and tertiary sector, respectively.

Cross-sectional dependence test
The examination of cross-sectional dependence (CD) in the panel data is of utmost importance, as the same might produce biased and inconsistent results (Phillips and Sul, 2003). Usually, the countries are connected via different channels such as economic, social, political, bilateral trade, and board sharing. These forms of associativity among the countries might result in cross-sectional dependence among the model variables. For this reason, the cross-sectional dependence (CD) test (Chudik and Pesaran, 2015) is applied to examine the presence of crosssectional dependence in the data. The cross-sectional dependence (CD) can be measured as follows: where, N= Cross-sections in panel, T= represents the time span, ρij = correlation coefficient of unit i and j.
Under the null hypothesis of weak cross-sectional dependence, it is assumed that the statistic is asymptotically distributed.

Unit root test
After observing the inter-country association, it is important to test whether the series are stable in the long run. For this purpose, the cross-sectional augmented Dickey-Fuller (i.e., CADF) (Pesaran, 2007) econometric approach has been adopted. This new generation procedure helps to establish the integration order. Eq. (7) exhibits the calculation procedure of this test where ν and z signify the lag-size and time-based mean interdependency, respectively.
This procedure generates the t-statistics by using the distinct ADF value. Based on the calculated values of this test, the cross-sectional Im-Pesaran-Shin test (CIPS) (Pesaran, 2007) provides the individually treated values based on the cross-country treatment. The expression presented in Eq. (8) generates the CIPS test results.

Cointegration test
By considering the possible cross-sectional dependence, Westerlund (2007) proposes a unique methodology to establish the long-run association between the variables. This test calculates the error correction value and generates the four cross-section-based values. Here, the significant values ascertain that the series are cointegrated and apt for the long-run examination. On the other hand, the acceptance of the null hypothesis signifies that the longrun cointegration among the series is missing. Eq. (9) presents the Westerlund test values.
Here, kt, and αi represent the constant term and adjustment speed, respectively. Also, the combinations of constant and trend, i.e., , are considered to represent the constant term. Pesaran (2006) presents a distinct solution technique to handle this mutual dependency, which can help in generating reliable results. The error term can be calculated by using the unobserved matrix (UFM) of the given factors, presented in Eq. (10).
By using the averages of the mutually dependent factors, the UFM will be calculated, which may efficiently handle the possible inter-dependency.

Data
The study is conducted for 100 countries over 1991-2019. These countries are segregated into four different income categories (i.e., low, lower-middle, upper-middle, and high), following the categorization provided by the World Bank (Serajuddin and Hamadeh, 2020). To get the detailed description, please refer to Table A.1 in the appendix. The data for total factor productivity has been obtained from Penn World  (Kovacs, 2018). The convergence in the employment rate during Industry 4.0 regime indicates the slowdown in job creation process (Gashenko et al., 2020). This slowdown in the job creation was catalyzed by the slowdown in manufacturing and other anthropogenic activities due to the incidence of COVID-19 outbreak. Hence, the COVID-  Table 2 for all the four groups of countries show that the CO2 emissions-TFP associations resemble the generally accepted inverted-U-shaped form of EKC. Now, it is worthwhile noting that except for the case of high-income countries, the turnaround points are outside the sample range. This indicates that though the prevailing economic growth trajectory is increasing the CO2 emissions, the growth rate of emission is decreasing, and the turnaround points will be achieved during the post-COVID period. This shows that the innovation-led economic growth trajectory being trodden by these nations is pro-environmental. Also, the turnaround point for low-income countries is the highest, followed by the ones of lower-middle and upper-middle income countries. This gives a comparative scenario between the prevailing policies in these countries. With the rise in income level, policymakers strive to transform the existing policies from pro-growth to pro-development. This distinction among the economic policies in these four groups of countries is revealed through the nature of turnaround points.
While all these countries depend on the innovation processes, it might be possible that the development of the innovation capabilities is not ingenious. Hence, the innovative solutions might be imported. Also, it is found that the impact of globalization on CO2 emissions is positive except for high-income countries. The highest impact is observed in the case of lowincome countries, followed by the lower-middle and upper-middle income countries. This scenario can be traced back to the "Pollution Haven hypothesis," i.e., the high-income countries try to reduce their environmental degradation by exporting the low-cost polluting technologies to the countries with a lower level of income. For this reason, it is expected that the impact of globalization on CO2 emissions might be negative for the high-income countries, and the study outcome is consistent with this phenomenon. Now, once these technologies are employed in the production, commercial electricity is required to run these solutions. As the fossil fuelbased energy solutions remain the predominant source of energy across the majority of the countries, it is expected that the energy usage patterns will have negative environmental consequences. The present study outcome reflects the same. However, the negative environmental impact has been observed the highest and lowest for the low-income and highincome countries, respectively. This situation might have arisen due to the highest and lowest share of renewable energy solutions in the energy mix of the high-income and low-income countries, respectively. Apart from the energy usage pattern in the industries, the domestic energy demand also caters to the end-energy use, leading to CO2 emissions. Hence, the impact of population on CO2 emissions is expected to be positive, and the study outcomes also reveal the same phenomenon. However, it is noteworthy to observe that the impact is highest in the case of the low-income countries and lowest for the upper-middle income and high-income countries. This situation can be attributed to the population growth characteristics of these nations, i.e., the low-income countries have the highest population growth, whereas it is lowest for the upper-middle income and high-income countries.
The structural transformation of the economy exhibits a positive impact on the CO2 emissions for the low, lower-middle, and upper-middle income countries. On the contrary, the effect is negative in high-income countries. This finding can be traced back to the impact of economic growth patterns on environmental degradation, and thereby, substantiating the policy void existing in the low-, lower-middle, and upper-middle income countries from the perspective of achieving environmental sustainability. This is an area where policy intervention might be necessary for these countries to make progress towards achieving the agenda 2030. After the CO2 emissions-economic growth association, the unemployment-economic growth association is discussed. The model outcomes reported in Table 3 for all the four groups show that the unemployment-TFP associations depict a U-shaped form. This segment of the results reveals that unemployment first shrinks with the rise in innovation-led economic growth and starts increasing after reaching a threshold. In a similar fashion with the previous case, it is observed that the turnaround point for low-income countries is the highest, followed by the ones of lower-middle, upper-middle, and high-income countries. The comparative analysis between the prevailing policies in these countries signifies that the turnaround points remain outside the sample range for all four cases. It indicates that the economic growth trajectory trodden by these countries can perhaps give rise to the issue of unemployment during the post-COVID period. When this finding is analyzed along with the previous segment of estimation results for CO2 emissions, a policy paradox is encountered. When the innovation-led economic growth trajectory is expected to bring environmental sustainability, the same growth trajectory compels these countries to depart from achieving social sustainability. This scenario can be

Robustness check with second-generation methods
In a globalized world, it might be possible that the countries might be connected via economic spillovers. Therefore, these associations cause the interdependency between economic growth drivers of different countries. This dependence leads to a specific estimation issue, i.e., crosssectional dependence. In the presence of cross-sectional dependence, the second-generation methodological approach should be adopted. Driven by this estimation agenda, first, the possibilities of cross-sectional dependence and slope heterogeneity are checked in the data. The test outcomes reported in Table 4 suggest the presence of cross-sectional dependence and panel heterogeneity in data. Hence, this evidence warrants the application of the second-generation methodological approach. The cross-sectional dependence in the data necessitates the incorporation of secondgeneration panel diagnostic tests for long-run coefficient estimation. The second-generation panel unit root test is conducted to understand the order of the integration among the model parameters. The results reported in Table 5 show that the model parameters to be integrated to first order. Based on the confirmation of order of integration among the model parameters, the confirmation of long-run association among them is validated through the second-generation panel cointegration test. The results reported in Table 6 demonstrate that the model parameters to be cointegrated, and this segment of the findings warrant the estimation of long-run coefficients.

Analysis of full sample
In this subsection, finally, the estimation for the entire sample is conducted using LSDV, CS-DL, and DCCE-GMM methods. The estimation results are reported in Tables 9 and 10. The model outcomes reported in Table 9   Apart from the environmental impact, the innovation-led economic growth trajectory experiences a social impact. The estimation outcomes of this impact are reported in Table 10.
The model outcomes reveal that the unemployment-TFP association resembles a U-shaped association, consistent across three estimation methods. This insight indicates a socially unsustainable economic growth trajectory, as unemployment rises after a decline and reaches However, this impact cannot be a sustainable one, given the high population growth in the destination countries. As both the impacts of the innovation-led economic growth have been discussed, along with the impacts of other relevant policy instruments, it is necessary to facilitate a holistic depiction by analyzing both scenarios together. Graphical representation of both the associations is provided in Figure 1, where the CO2 emissions-TFP association is inverted U-shaped and the unemployment-TFP association is U-shaped. As the rise in TFP affects the CO2 emissions and unemployment in opposite ways, the policy trade-off situation becomes evident in Figure 1. As the best solution might not be achievable in such a scenario, an optimum policy mix between these two competing objectives needs to be devised. In Figure 2, a vivid depiction of this situation is presented. It is assumed that both CO2 emissions and unemployment are at equilibrium at point 1 . At this point, CO2 emissions and unemployment are denoted by 1 and 1 , respectively. This equilibrium is achieved during the pre-COVID situation.
Beyond this point, both CO2 emissions and unemployment start exhibiting a decline in their growth rates, and both these target policy parameters achieve their respective thresholds. It is worthwhile to mention that at any point in time, either of the two policy parameters can be considered due to the opposite evolutionary impacts of TFP on these parameters. At any particular value of TFP at time t (between pre-and post-COVID periods), the value of CO2 emissions is presented as * , and the value of unemployment is denoted as * . Now, towards the left side of time t, CO2 emission is rising and unemployment is falling, while towards the right side of time t, CO2 emission is falling and unemployment is rising beyond the threshold limit. Also, it is observed that both * and * appear again at time and , on the CO2 emissions-TFP and Unemployment-TFP graphs, respectively. Now, if these two scenarios are compared with the ones at time t, then the following conditions can be derived:

Conclusion and policy implications
The dual impacts of innovation of CO2 emissions and unemployment are analyzed at a global scale, and results indicate that innovation-CO2 emissions association follows an inverted Ushaped form, whereas innovation-unemployment association follows a U-shaped form. The study outcomes divulge that a socio-ecological policy trade-off exists at the aggregate level as well as at various income levels. Based on the outcomes, a policy framework is recommended for addressing this policy trade-off.

Implications for theory
This study has made an attempt to extend the famous "Capital-labor substitution" principle proposed by Arrow et al. (1961). The seminal work by Arrow et al. (1961) shows that the economic efficiency in the international trade might be achieved by substituting capital with human labor. It gave an indication that the human laborers will be substituted by the capital following a shape convex to the origin. However, the notion of technological development during that study was limited to the manufacturing sector, which was characteristically laborintensive. With the advent of technological innovations in the age of Industry 4.0 and growth of the service sector, the notion of technological development has undergone a transformation.
Hence, the work of Arrow et al. (1961) might be extended from a convex curve, following the evolutionary nature of technological innovations. Moreover, the "Capital-labor substitution" principle majorly looked into the unidimensional aspect of capital. In the SDG regime, the innovation is expected to play a dual rolein environmental and social dimensions. Therefore, the "Capital-labor substitution" principle required an extension from the socio-ecological tradeoff perspective. hypothesis gives a leverage to analyze the nonlinear evolutionary impact over a temporal frame, and hence, this framework can be utilized as a policy forecasting tool. Going beyond the traditional environmental impact assessment, this study has shown the power of EKC hypothesis in analyzing the policy tradeoff. In order to bring additional insights to the analysis, the cubic specification of the EKC hypothesis can be utilized . In that process, capturing the movement of the inflection point appearing between the two turnaround points can uncover further intuitions regarding the possible policy tradeoffs.

Implications for policymakers
As the innovation influences both CO2 emissions and unemployment, it is not possible to arrive at the best solution for either of the cases due to the opposite nature of the impacts. Therefore, this policy trade-off requires an optimum solution, which will bring the social and ecological impacts of innovation at a certain equilibrium. Hence, the objective of the policy framework should be focused at achieving this equilibrium during the post-COVID period. Now, this policy framework can be designed following a phase-wise schedule. As the impact of technological innovation will be immediately visible on the CO2 emissions, therefore the first phase of the policy framework should focus on the ecological impact of innovation. For the innovation to achieve its full potential in CO2 emissions reduction, the policymakers should adopt appropriate measures to reduce the reluctance of the industrial sector to accept these solutions. Moreover, as continued dependence on the fossil fuel solutions might reduce the potential impact of innovation on the CO2 emissions, a gradual shift from the nonrenewable to renewable energy sources is also required. A drastic fall in the anthropogenic activities during the COVID-19 outbreak has highlighted the emergence of service sector firms. Now the policymakers might need to bring certain policy interventions for their respective nations towards being recognized as service-oriented economies for retaining the environmental quality. Now, in such a situation, overnight sectoral overhaul might create deterrence to the prevailing economic growth pattern of these countries. Therefore, policymakers might need to use the existing financialization channels in such a way, so that the firms can embrace the innovations in a hassle-free manner within a predefined time. For this purpose, the financial institutions can introduce the concept of discriminatory interest rates on loans and advances for availing the innovative solutions, whereas this discrimination might be based on the carbon footprint of the firms. Hence, given a fixed period, the dirtier firms will be compelled to avail the solutions at a higher rate of interest, whereas the cleaner firms will enjoy a lower rate of interest. Thus, the economic system in the countries will encourage the adoption of cleaner energy solutions and innovation. During this finance-driven transition, it should be remembered that the higher bracket of the interest rate should not be higher than the existing average cost of capital of the firms. Otherwise, it might dissuade the firms to embrace the innovation for improving the quality of their production systems. Additionally, they might stop the business operations. Hence, the rate of interest should not be a deterrence in the innovation adoption process.
Once this phase becomes operational, the second phase of the framework should be designed to internalize the negative social externalities. As the rising diffusion of innovation is starting to replace the human labors after the deployment and implementation period, there is a requirement of an early policy intervention. When the firms start implementing the innovative solutions in their existing production processes, there should be an upper threshold of innovation for the firms, beyond which they won't be able to replace human labour with technology. Following Eqs. (3) and (4), the level of TFP can be denoted as per the following: 1 * = [{−( 1 − 1 ) ± √( 1 − 1 ) 2 − 4( 0 − 0 )( 2 − 2 )}/2( 2 − 2 )] Here, 0 , 1 , 2 , 0 , 1 , 2 ≠ 0 and, ( 1 − 1 ) 2 ≥ 4( 0 − 0 )( 2 − 2 ) At the point denoted in Eq. (11), the level of TFP to be achieved by any firm allows it to tread along the long-run equilibrium growth trajectory. Moving beyond this point might help the firm to gain a short-run economic profit at the cost of employment. Now, the rise in consequential unemployment might create a demand pressure in the economy, leading to the reduction in supply and production. Treading along the long-run equilibrium growth path necessitates the production to continue. For this purpose, certain short-run economic losses need to be incurred. Therefore, the policymakers need to ensure that the policy intervention point should be achieved immediately after the first phase of the policy framework is implemented to keep the economic growth trajectory intact. The accomplishment of the first phase of the policy framework will help these countries in achieving the objectives of SDG 13, whereas the second phase will allow these nations to achieve the objectives of SDG 8. Thus, this policy framework ensures sustainable development in the post-COVID scenario.
While the core policy framework helps these nations to avoid the policy trade-off, the tangential policy framework helps to sustain the core policy framework. After the first two phases of the policy framework being operational, policymakers need to gradually control the population growth rate. If the population growth rate is higher than the growth rate of job opportunities, it creates the unemployment issue. As a result, the core policy framework might not be able to achieve its full potential. Hence, strict population control mechanisms should be devised by policymakers. Moreover, the policymakers should encourage the start-up ventures with these technologies to diffuse the innovations in a more effective manner. However, the capital allocation should be done in such a way so that the firm can generate enough employment, not fully replacing the labor with technology. For institutionalizing these solutions, the educational curriculum should be amended. It can raise the students' awareness of the latest technological developments and innovations across the nations and the social and ecological benefits of innovation. Thus, it will help the nations in accomplishing the objectives of SDG 9 (industry, innovation and infrastructure) and SDG 4 (quality education).

Policy caveats and assumptions
Discussion of a policy framework is seemingly incomplete without mentioning the caveats and assumptions behind the framework. Understanding these two aspects is necessary, as their nonfulfillment might hinder the policy framework from reaching its full potential (Cheng et al., 2021a, b). First, during bringing discrimination in the interest rate, the slabs should be made close so that many firms can be encapsulated. Second, policymakers need to introduce rehabilitation policies and vocation centers for the labor employed in the traditional fossil fuelbased energy generation sector. Third, the rent-seeking mechanism in the bureaucratic system should be brought to the minimum, as such incidents of corruption hinder the diffusion of innovation within and across the borders.

Limitations and future projections
Though this study has introduced a critical policy dimension by describing the socio-ecological policy trade-off initiated by innovation, the study might suffer from certain limitations. One of the major limitations of the study is that only the output indicator of innovation is considered, while various other forms of innovation (e.g., social innovation, environmental innovation) have not been incorporated. Though theoretically proven, putting forth a generalized view of innovation might yield different results in the empirical pursuit. Saying about this limitation, this is also needed to clarify that the policy framework introduced in this study can serve as a baseline policy approach for addressing the policy trade-off in any context. Moreover, the framework is flexible to accommodate any additional policy instrument, which might be contextually suitable. This flexibility and generalizability have made this policy framework a contribution to the literature. Future studies in this direction can be carried out by considering various forms of innovation and how those forms can demonstrate socio-ecological trade-offs in various contexts.