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• Published: 06 May 2020

Deforestation and world population sustainability: a quantitative analysis

• Mauro Bologna 1   na1 &
• Gerardo Aquino 2 , 3 , 4   na1  

Scientific Reports volume  10 , Article number:  7631 ( 2020 ) Cite this article

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• Applied mathematics
• Environmental impact
• Population dynamics
• Statistical physics, thermodynamics and nonlinear dynamics

In this paper we afford a quantitative analysis of the sustainability of current world population growth in relation to the parallel deforestation process adopting a statistical point of view. We consider a simplified model based on a stochastic growth process driven by a continuous time random walk, which depicts the technological evolution of human kind, in conjunction with a deterministic generalised logistic model for humans-forest interaction and we evaluate the probability of avoiding the self-destruction of our civilisation. Based on the current resource consumption rates and best estimate of technological rate growth our study shows that we have very low probability, less than 10% in most optimistic estimate, to survive without facing a catastrophic collapse.

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Introduction

In the last few decades, the debate on climate change has assumed global importance with consequences on national and global policies. Many factors due to human activity are considered as possible responsible of the observed changes: among these water and air contamination (mostly greenhouse effect) and deforestation are the mostly cited. While the extent of human contribution to the greenhouse effect and temperature changes is still a matter of discussion, the deforestation is an undeniable fact. Indeed before the development of human civilisations, our planet was covered by 60 million square kilometres of forest 1 . As a result of deforestation, less than 40 million square kilometres currently remain 2 . In this paper, we focus on the consequence of indiscriminate deforestation.

Trees’ services to our planet range from carbon storage, oxygen production to soil conservation and water cycle regulation. They support natural and human food systems and provide homes for countless species, including us, through building materials. Trees and forests are our best atmosphere cleaners and, due to the key role they play in the terrestrial ecosystem, it is highly unlikely to imagine the survival of many species, including ours, on Earth without them. In this sense, the debate on climate change will be almost obsolete in case of a global deforestation of the planet. Starting from this almost obvious observation, we investigate the problem of the survival of humanity from a statistical point of view. We model the interaction between forests and humans based on a deterministic logistic-like dynamics, while we assume a stochastic model for the technological development of the human civilisation. The former model has already been applied in similar contexts 3 , 4 while the latter is based on data and model of global energy consumption 5 , 6 used as a proxy for the technological development of a society. This gives solidity to our discussion and we show that, keeping the current rate of deforestation, statistically the probability to survive without facing a catastrophic collapse, is very low. We connect such probability to survive to the capability of humankind to spread and exploit the resources of the full solar system. According to Kardashev scale 7 , 8 , which measures a civilisation’s level of technological advancement based on the amount of energy they are able to use, in order to spread through the solar system we need to be able to harness the energy radiated by the Sun at a rate of ≈4 × 10 26 Watt. Our current energy consumption rate is estimated in ≈10 13 Watt 9 . As showed in the subsections “Statistical Model of technological development” and “Numerical results” of the following section, a successful outcome has a well defined threshold and we conclude that the probability of avoiding a catastrophic collapse is very low, less than 10% in the most optimistic estimate.

Model and Results

Deforestation.

The deforestation of the planet is a fact 2 . Between 2000 and 2012, 2.3 million Km 2 of forests around the world were cut down 10 which amounts to 2 × 10 5 Km 2 per year. At this rate all the forests would disappear approximatively in 100–200 years. Clearly it is unrealistic to imagine that the human society would start to be affected by the deforestation only when the last tree would be cut down. The progressive degradation of the environment due to deforestation would heavily affect human society and consequently the human collapse would start much earlier.

Curiously enough, the current situation of our planet has a lot in common with the deforestation of Easter Island as described in 3 . We therefore use the model introduced in that reference to roughly describe the humans-forest interaction. Admittedly, we are not aiming here for an exact exhaustive model. It is probably impossible to build such a model. What we propose and illustrate in the following sections, is a simplified model which nonetheless allows us to extrapolate the time scales of the processes involved: i.e. the deterministic process describing human population and resource (forest) consumption and the stochastic process defining the economic and technological growth of societies. Adopting the model in 3 (see also 11 ) we have for the humans-forest dynamics

where N represent the world population and R the Earth surface covered by forest. β is a positive constant related to the carrying capacity of the planet for human population, r is the growth rate for humans (estimated as r  ~ 0.01 years −1 ) 12 , a 0 may be identified as the technological parameter measuring the rate at which humans can extract the resources from the environment, as a consequence of their reached technological level. r ’ is the renewability parameter representing the capability of the resources to regenerate, (estimated as r ’ ~ 0.001 years −1 ) 13 , R c the resources carrying capacity that in our case may be identified with the initial 60 million square kilometres of forest. A closer look at this simplified model and at the analogy with Easter Island on which is based, shows nonetheless, strong similarities with our current situation. Like the old inhabitants of Easter Island we too, at least for few more decades, cannot leave the planet. The consumption of the natural resources, in particular the forests, is in competition with our technological level. Higher technological level leads to growing population and higher forest consumption (larger a 0 ) but also to a more effective use of resources. With higher technological level we can in principle develop technical solutions to avoid/prevent the ecological collapse of our planet or, as last chance, to rebuild a civilisation in the extraterrestrial space (see section on the Fermi paradox). The dynamics of our model for humans-forest interaction in Eqs. ( 1 , 2 ), is typically characterised by a growing human population until a maximum is reached after which a rapid disastrous collapse in population occurs before eventually reaching a low population steady state or total extinction. We will use this maximum as a reference for reaching a disastrous condition. We call this point in time the “no-return point” because if the deforestation rate is not changed before this time the human population will not be able to sustain itself and a disastrous collapse or even extinction will occur. As a first approximation 3 , since the capability of the resources to regenerate, r ′, is an order of magnitude smaller than the growing rate for humans, r , we may neglect the first term in the right hand-side of Eq. ( 2 ). Therefore, working in a regime of the exploitation of the resources governed essentially by the deforestation, from Eq. ( 2 ) we can derive the rate of tree extinction as

The actual population of the Earth is N  ~ 7.5 × 10 9 inhabitants with a maximum carrying capacity estimated 14 of N c  ~ 10 10 inhabitants. The forest carrying capacity may be taken as 1 R c  ~ 6 × 10 7 Km 2 while the actual surface of forest is \(R\lesssim 4\times {10}^{7}\) Km 2 . Assuming that β is constant, we may estimate this parameter evaluating the equality N c ( t ) =  βR ( t ) at the time when the forests were intact. Here N c ( t ) is the instantaneous human carrying capacity given by Eq. ( 1 ). We obtain β  ~  N c / R c  ~ 170.

In alternative we may evaluate β using actual data of the population growth 15 and inserting it in Eq. ( 1 ). In this case we obtain a range \(700\lesssim \beta \lesssim 900\) that gives a slightly favourable scenario for the human kind (see below and Fig.  4 ). We stress anyway that this second scenario depends on many factors not least the fact that the period examined in 15 is relatively short. On the contrary β  ~ 170 is based on the accepted value for the maximum human carrying capacity. With respect to the value of parameter a 0 , adopting the data relative to years 2000–2012 of ref. 10 ,we have

The time evolution of system ( 1 ) and ( 2 ) is plotted in Figs.  1 and 2 . We note that in Fig.  1 the numerical value of the maximum of the function N ( t ) is N M  ~ 10 10 estimated as the carrying capacity for the Earth population 14 . Again we have to stress that it is unrealistic to think that the decline of the population in a situation of strong environmental degradation would be a non-chaotic and well-ordered decline, that is also way we take the maximum in population and the time at which occurs as the point of reference for the occurrence of an irreversible catastrophic collapse, namely a ‘no-return’ point.

On the left: plot of the solution of Eq. ( 1 ) with the initial condition N 0  = 6 × 10 9 at initial time t  = 2000 A.C. On the right: plot of the solution of Eq. ( 2 ) with the initial condition R 0  = 4 × 10 7 . Here β  = 700 and a 0  = 10 −12 .

On the left: plot of the solution of Eq. ( 1 ) with the initial condition N 0  = 6 × 10 9 at initial time t  = 2000 A.C. On the right: plot of the solution of Eq. ( 2 ) with the initial condition R 0  = 4 × 10 7 . Here β  = 170 and a 0  = 10 −12 .

Statistical model of technological development

According to Kardashev scale 7 , 8 , in order to be able to spread through the solar system, a civilisation must be capable to build a Dyson sphere 16 , i.e. a maximal technological exploitation of most the energy from its local star, which in the case of the Earth with the Sun would correspond to an energy consumption of E D  ≈ 4 × 10 26 Watts, we call this value Dyson limit. Our actual energy consumption is estimated in E c  ≈ 10 13 Watts (Statistical Review of World Energy source) 9 . To describe our technological evolution, we may roughly schematise the development as a dichotomous random process

where T is the level of technological development of human civilisation that we can also identify with the energy consumption. α is a constant parameter describing the technological growth rate (i.e. of T ) and ξ ( t ) a random variable with values 0, 1. We consider therefore, based on data of global energy consumption 5 , 6 an exponential growth with fluctuations mainly reflecting changes in global economy. We therefore consider a modulated exponential growth process where the fluctuations in the growth rate are captured by the variable ξ ( t ). This variable switches between values 0, 1 with waiting times between switches distributed with density ψ ( t ). When ξ ( t ) = 0 the growth stops and resumes when ξ switches to ξ ( t ) = 1. If we consider T more strictly as describing the technological development, ξ ( t ) reflects the fact that investments in research can have interruptions as a consequence of alternation of periods of economic growth and crisis. With the following transformation,

differentiating both sides respect to t and using Eq. ( 5 ), we obtain for the transformed variable W

where \(\bar{\xi }(t)=2[\xi (t)-\langle \xi \rangle ]\) and 〈ξ 〉 is the average of ξ ( t ) so that \(\bar{\xi }(t)\) takes the values ±1.

The above equation has been intensively studied, and a general solution for the probability distribution P ( W , t ) generated by a generic waiting time distribution can be found in literature 17 . Knowing the distribution we may evaluate the first passage time distribution in reaching the necessary level of technology to e.g. live in the extraterrestrial space or develop any other way to sustain population of the planet. This characteristic time has to be compared with the time that it will take to reach the no-return point. Knowing the first passage time distribution 18 we will be able to evaluate the probability to survive for our civilisation.

If the dichotomous process is a Poissonian process with rate γ then the correlation function is an exponential, i.e.

and Eq. ( 7 ) generates for the probability density the well known telegrapher’s equation

We note that the approach that we are following is based on the assumption that at random times, exponentially distributed with rate γ , the dichotomous variable \(\bar{\xi }\) changes its value. With this assumption the solution to Eq. ( 9 ) is

where I n ( z ) are the modified Bessel function of the first kind. Transforming back to the variable T we have

where for sake of compactness we set

In Laplace transform we have

The first passage time distribution, in laplace transform, is evaluated as 19

Inverting the Laplace transform we obtain

which is confirmed (see Fig.  3 ) by numerical simulations. The time average to get the point x for the first time is given by

which interestingly is double the time it would take if a pure exponential growth occurred, depends on the ratio between final and initial value of T and is independent of γ . We also stress that this result depends on parameters directly related to the stage of development of the considered civilisation, namely the starting value T 1 , that we assume to be the energy consumption E c of the fully industrialised stage of the civilisation evolution and the final value T , that we assume to be the Dyson limit E D , and the technological growth rate α . For the latter we may, rather optimistically, choose the value α  = 0.345, following the Moore Law 20 (see next section). Using the data above, relative to our planet’s scenario, we obtain the estimate of 〈 t 〉 ≈ 180 years. From Figs.  1 and 2 we see that the estimate for the no-return time are 130 and 22 years for β  = 700 and β  = 170 respectively, with the latter being the most realistic value. In either case, these estimates based on average values, being less than 180 years, already portend not a favourable outcome for avoiding a catastrophic collapse. Nonetheless, in order to estimate the actual probability for avoiding collapse we cannot rely on average values, but we need to evaluate the single trajectories, and count the ones that manage to reach the Dyson limit before the ‘no-return point’. We implement this numerically as explained in the following.

(Left) Comparison between theoretical prediction of Eq. ( 15 ) (black curve) and numerical simulation of Eq. ( 3 ) (cyan curve) for γ  = 4 (arbitrary units). (Right) Comparison between theoretical prediction of Eq. ( 15 ) (red curve) and numerical simulation of Eq. ( 3 ) (black curve) for γ  = 1/4 (arbitrary units).

(Left panel) Probability p suc of reaching Dyson value before reaching “no-return” point as function of α and a for β  = 170. Parameter a is expressed in Km 2 ys −1 . (Right panel) 2D plot of p suc for a  = 1.5 × 10 −4 Km 2 ys −1 as a function of α . Red line is p suc for β  = 170. Black continuous lines (indistinguishable) are p suc for β  = 300 and 700 respectively (see also Fig.  6 ). Green dashed line indicates the value of α corresponding to Moore’s law.

Numerical results

We run simulations of Eqs. ( 1 ), ( 2 ) and ( 5 ) simultaneously for different values of of parameters a 0 and α for fixed β and we count the number of trajectories that reach Dyson limit before the population level reaches the “no-return point” after which rapid collapse occurs. More precisely, the evolution of T is stochastic due to the dichotomous random process ξ ( t ), so we generate the T ( t ) trajectories and at the same time we follow the evolution of the population and forest density dictated by the dynamics of Eqs. ( 1 ), ( 2 ) 3 until the latter dynamics reaches the no-return point (maximum in population followed by collapse). When this happens, if the trajectory in T ( t ) has reached the Dyson limit we count it as a success, otherwise as failure. This way we determine the probabilities and relative mean times in Figs.  5 , 6 and 7 . Adopting a weak sustainability point of view our model does not specify the technological mechanism by which the successful trajectories are able to find an alternative to forests and avoid collapse, we leave this undefined and link it exclusively and probabilistically to the attainment of the Dyson limit. It is important to notice that we link the technological growth process described by Eq. ( 5 ) to the economic growth and therefore we consider, for both economic and technological growth, a random sequence of growth and stagnation cycles, with mean periods of about 1 and 4 years in accordance with estimates for the driving world economy, i.e. the United States according to the National Bureau of Economic Research 21 .

Average time τ (in years) to reach Dyson value before hitting “no-return” point (success, left) and without meeting Dyson value (failure, right) as function of α and a for β  = 170. Plateau region (left panel) where τ  ≥ 50 corresponds to diverging τ , i.e. Dyson value not being reached before hitting “no-return” point and therefore failure. Plateau region at τ  = 0 (right panel), corresponds to failure not occurring, i.e. success. Parameter a is expressed in Km 2 ys −1 .

Probability p suc of reaching Dyson value before hitting “no-return” point as function of α and a for β  = 300 (left) and 700 (right). Parameter a is expressed in Km 2 ys −1 .

Probability of reaching Dyson value p suc before reaching “no-return” point as function of β and α for a  = 1.5 × 10 −4 Km 2 ys −1 .

In Eq. ( 1 , 2 ) we redefine the variables as N ′ =  N / R W and R ′ =  R / R W with \({R}_{W}\simeq 150\times {10}^{6}\,K{m}^{2}\) the total continental area, and replace parameter a 0 accordingly with a  =  a 0  ×  R W  = 1.5 × 10 −4 Km 2 ys −1 . We run simulations accordingly starting from values \({R{\prime} }_{0}\) and \({N{\prime} }_{0}\) , based respectively on the current forest surface and human population. We take values of a from 10 −5 to 3 × 10 −4 Km 2 ys −1 and for α from 0.01 ys −1 to 4.4 ys −1 . Results are shown in Figs.  4 and 6 . Figure  4 shows a threshold value for the parameter α , the technological growth rate, above which there is a non-zero probability of success. This threshold value increases with the value of the other parameter a . As shown in Fig.  7 this values depends as well on the value of β and higher values of β correspond to a more favourable scenario where the transition to a non-zero probability of success occurs for smaller α , i.e. for smaller, more accessible values, of technological growth rate. More specifically, left panel of Fig.  4 shows that, for the more realistic value β  = 170, a region of parameter values with non-zero probability of avoiding collapse corresponds to values of α larger than 0.5. Even assuming that the technological growth rate be comparable to the value α  = log(2)/2 = 0.345 ys −1 , given by the Moore Law (corresponding to a doubling in size every two years), therefore, it is unlikely in this regime to avoid reaching the the catastrophic ‘no-return point’. When the realistic value of a  = 1.5 × 10 4 Km 2 ys −1 estimated from Eq. ( 4 ), is adopted, in fact, a probability less than 10% is obtained for avoiding collapse with a Moore growth rate, even when adopting the more optimistic scenario corresponding to β  = 700 (black curve in right panel of Fig.  4 ). While an α larger than 1.5 is needed to have a non-zero probability of avoiding collapse when β  = 170 (red curve, same panel). As far as time scales are concerned, right panel of Fig.  5 shows for β  = 170 that even in the range α  > 0.5, corresponding to a non-zero probability of avoiding collapse, collapse is still possible, and when this occurs, the average time to the ‘no-return point’ ranges from 20 to 40 years. Left panel in same figure, shows for the same parameters, that in order to avoid catastrophe, our society has to reach the Dyson’s limit in the same average amount of time of 20–40 years.

In Fig.  7 we show the dependence of the model on the parameter β for a  = 1.5 × 10 −4 .

We run simulations of Eqs. ( 1 ), ( 2 ) and ( 5 ) simultaneously for different values of of parameters a 0 and α depending on β as explained in Methods and Results to generate Figs.  5 , 6 and 7 . Equations ( 1 ), ( 2 ) are integrated via standard Euler method. Eq. ( 5 ) is integrated as well via standard Euler method between the random changes of the variable ξ . The stochastic dichotomous process ξ is generated numerically in the following way: using the random number generator from gsl library we generate the times intervals between the changes of the dichotomous variable ξ  = 0, 1, with an exponential distribution(with mean values of 1 and 4 years respectively), we therefore obtain a time series of 0 and 1 for each trajectory. We then integrate Eq. ( 5 ) in time using this time series and we average over N  = 10000 trajectories. The latter procedure is used to carry out simulations in Figs.  3 and 4 as well in order to evaluate the first passage time probabilities. All simulations are implemented in C++.

Fermi paradox

In this section we briefly discuss a few considerations about the so called Fermi paradox that can be drawn from our model. We may in fact relate the Fermi paradox to the problem of resource consumption and self destruction of a civilisation. The origin of Fermi paradox dates back to a casual conversation about extraterrestrial life that Enrico Fermi had with E. Konopinski, E. Teller and H. York in 1950, during which Fermi asked the famous question: “where is everybody?”, since then become eponymous for the paradox. Starting from the closely related Drake equation 22 , 23 , used to estimate the number of extraterrestrial civilisations in the Milky Way, the debate around this topic has been particularly intense in the past (for a more comprehensive covering we refer to Hart 24 , Freitas 25 and reference therein). Hart’s conclusion is that there are no other advanced or ‘technological’ civilisations in our galaxy as also supported recently by 26 based on a careful reexamination of Drake’s equation. In other words the terrestrial civilisation should be the only one living in the Milk Way. Such conclusions are still debated, but many of Hart’s arguments are undoubtedly still valid while some of them need to be rediscussed or updated. For example, there is also the possibility that avoiding communication might actually be an ‘intelligent’ choice and a possible explanation of the paradox. On several public occasions, in fact, Professor Stephen Hawking suggested human kind should be very cautious about making contact with extraterrestrial life. More precisely when questioned about planet Gliese 832c’s potential for alien life he once said: “One day, we might receive a signal from a planet like this, but we should be wary of answering back”. Human history has in fact been punctuated by clashes between different civilisations and cultures which should serve as caveat. From the relatively soft replacement between Neanderthals and Homo Sapiens (Kolodny 27 ) up to the violent confrontation between native Americans and Europeans, the historical examples of clashes and extinctions of cultures and civilisations have been quite numerous. Looking at human history Hawking’s suggestion appears as a wise warning and we cannot role out the possibility that extraterrestrial societies are following similar advice coming from their best minds.

With the help of new technologies capable of observing extrasolar planetary systems, searching and contacting alien life is becoming a concrete possibility (see for example Grimaldi 28 for a study on the chance of detecting extraterrestrial intelligence), therefore a discussion on the probability of this occurring is an important opportunity to assess also our current situation as a civilisation. Among Hart’s arguments, the self-destruction hypothesis especially needs to be rediscussed at a deeper level. Self-destruction following environmental degradation is becoming more and more an alarming possibility. While violent events, such as global war or natural catastrophic events, are of immediate concern to everyone, a relatively slow consumption of the planetary resources may be not perceived as strongly as a mortal danger for the human civilisation. Modern societies are in fact driven by Economy, and, without giving here a well detailed definition of “economical society”, we may agree that such a kind of society privileges the interest of its components with less or no concern for the whole ecosystem that hosts them (for more details see 29 for a review on Ecological Economics and its criticisms to mainstream Economics). Clear examples of the consequences of this type of societies are the international agreements about Climate Change. The Paris climate agreement 30 , 31 is in fact, just the last example of a weak agreement due to its strong subordination to the economic interests of the single individual countries. In contraposition to this type of society we may have to redefine a different model of society, a “cultural society”, that in some way privileges the interest of the ecosystem above the individual interest of its components, but eventually in accordance with the overall communal interest. This consideration suggests a statistical explanation of Fermi paradox: even if intelligent life forms were very common (in agreement with the mediocrity principle in one of its version 32 : “there is nothing special about the solar system and the planet Earth”) only very few civilisations would be able to reach a sufficient technological level so as to spread in their own solar system before collapsing due to resource consumption.

We are aware that several objections can be raised against this argument and we discuss below the one that we believe to be the most important. The main objection is that we do not know anything about extraterrestrial life. Consequently, we do not know the role that a hypothetical intelligence plays in the ecosystem of the planet. For example not necessarily the planet needs trees (or the equivalent of trees) for its ecosystem. Furthermore the intelligent form of life could be itself the analogous of our trees, so avoiding the problem of the “deforestation” (or its analogous). But if we assume that we are not an exception (mediocrity principle) then independently of the structure of the alien ecosystem, the intelligent life form would exploit every kind of resources, from rocks to organic resources (animal/vegetal/etc), evolving towards a critical situation. Even if we are at the beginning of the extrasolar planetology, we have strong indications that Earth-like planets have the volume magnitude of the order of our planet. In other words, the resources that alien civilisations have at their disposal are, as order of magnitude, the same for all of them, including ourselves. Furthermore the mean time to reach the Dyson limit as derived in Eq.  6 depends only on the ratio between final and initial value of T and therefore would be independent of the size of the planet, if we assume as a proxy for T energy consumption (which scales with the size of the planet), producing a rather general result which can be extended to other civilisations. Along this line of thinking, if we are an exception in the Universe we have a high probability to collapse or become extinct, while if we assume the mediocrity principle we are led to conclude that very few civilisations are able to reach a sufficient technological level so as to spread in their own solar system before the consumption of their planet’s resources triggers a catastrophic population collapse. The mediocrity principle has been questioned (see for example Kukla 33 for a critical discussion about it) but on the other hand the idea that the humankind is in some way “special” in the universe has historically been challenged several times. Starting with the idea of the Earth at the centre of the universe (geocentrism), then of the solar system as centre of the universe (Heliocentrism) and finally our galaxy as centre of the universe. All these beliefs have been denied by the facts. Our discussion, being focused on the resource consumption, shows that whether we assume the mediocrity principle or our “uniqueness” as an intelligent species in the universe, the conclusion does not change. Giving a very broad meaning to the concept of cultural civilisation as a civilisation not strongly ruled by economy, we suggest for avoiding collapse 34 that only civilisations capable of such a switch from an economical society to a sort of “cultural” society in a timely manner, may survive. This discussion leads us to the conclusion that, even assuming the mediocrity principle, the answer to “Where is everybody?” could be a lugubrious “(almost) everyone is dead”.

Conclusions

In conclusion our model shows that a catastrophic collapse in human population, due to resource consumption, is the most likely scenario of the dynamical evolution based on current parameters. Adopting a combined deterministic and stochastic model we conclude from a statistical point of view that the probability that our civilisation survives itself is less than 10% in the most optimistic scenario. Calculations show that, maintaining the actual rate of population growth and resource consumption, in particular forest consumption, we have a few decades left before an irreversible collapse of our civilisation (see Fig.  5 ). Making the situation even worse, we stress once again that it is unrealistic to think that the decline of the population in a situation of strong environmental degradation would be a non-chaotic and well-ordered decline. This consideration leads to an even shorter remaining time. Admittedly, in our analysis, we assume parameters such as population growth and deforestation rate in our model as constant. This is a rough approximation which allows us to predict future scenarios based on current conditions. Nonetheless the resulting mean-times for a catastrophic outcome to occur, which are of the order of 2–4 decades (see Fig.  5 ), make this approximation acceptable, as it is hard to imagine, in absence of very strong collective efforts, big changes of these parameters to occur in such time scale. This interval of time seems to be out of our reach and incompatible with the actual rate of the resource consumption on Earth, although some fluctuations around this trend are possible 35 not only due to unforeseen effects of climate change but also to desirable human-driven reforestation. This scenario offers as well a plausible additional explanation to the fact that no signals from other civilisations are detected. In fact according to Eq. ( 16 ) the mean time to reach Dyson sphere depends on the ratio of the technological level T and therefore, assuming energy consumption (which scales with the size of the planet) as a proxy for T , such ratio is approximately independent of the size of the planet. Based on this observation and on the mediocrity principle, one could extend the results shown in this paper, and conclude that a generic civilisation has approximatively two centuries starting from its fully developed industrial age to reach the capability to spread through its own solar system. In fact, giving a very broad meaning to the concept of cultural civilisation as a civilisation not strongly ruled by economy, we suggest that only civilisations capable of a switch from an economical society to a sort of “cultural” society in a timely manner, may survive.

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Environmental sustainability and pollution prevention

• Published: 03 July 2018
• Volume 25 , pages 18223–18225, ( 2018 )

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• Farah Bouhamed 1 ,
• Mabrouk Elloussaief 1 &
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Environmental sustainability implies meeting our current needs without jeopardizing the right and the ability of future generations to meet theirs. Opportunities should be identified and taken to reduce the production of wastes and the use of toxic materials, to prevent soil, water, and air pollution and to conserve and reuse resources, as feasible. Environmental pollution with its health impacts is a key issue for sustainable environment (United Nations General Assembly 1987 ). Sustainability and sustainable development focuses on balancing that fine line between competing needs, our need to move forward technologically and economically, and the needs to protect the environments in which we and others live. Sustainability is not just about the environment (Kates et al. 2005 ); it’s also about our health as a society in ensuring that no people or areas of life suffer as a result of environmental legislation, and it’s also about examining the longer term effects of the actions humanity takes and asking questions about how it may be improved (World Commission on Environment and Development 1987 ). Sustainability has become a wide-ranging term that can be applied to almost every facet of life on Earth, from local to a global scale and over various time periods. Long-lived and healthy wetlands and forests are examples of sustainable biological systems. Invisible chemical cycles redistribute water, oxygen, nitrogen, and carbon through the world’s living and non-living systems and have sustained life since the beginning of time. As the earth’s human population has increased, natural ecosystems have declined and a change in the balance of natural cycles has had a negative impact on both humans and other living systems (Hawken 2007 ).

Pollution prevention reduces the amount of pollution generated by a process (industry, agriculture, or consumers). Pollution-control strategies, in general, seek to manage a pollutant after it is emitted and reduce its impact upon the environment; the pollution prevention approach, however, seeks to increase the efficiency of a process (Sherman et al. 2016 ), hence reducing the amount of pollution generated at its source. Although there is wide agreement that source reduction is the preferred strategy, some professionals also use the term pollution prevention to include pollution reduction.

With increasing human population, pollution has become a great concern. Pollution from human activities is a problem that does not have to be inevitable. With a comprehensive pollution prevention program, most pollution can be reduced, reused, or prevented. Reducing and managing pollution may decrease its health impacts (Thiel et al. 2015 ).

Pollution prevention however is a key issue to sustainability. Pollution results from waste. The best way to deal with pollution is to prevent it from being created in the first place. This means finding new efficiencies, doing things smarter, and valuing every resource. Understanding how waste is produced and how it can be minimized, or even prevented, is the first step to reduce waste and protect our environment; in that way, pollution prevention is an essential component of sustainability.

Fundamental ideas of preventing pollution rather than fixing problems are essential for efficient, economically viable manufacturing, providing services, and addressing many environmental problems (Jorgenson and Wilcoxen 1990 ).

With new business tools, new materials, and new approaches, it is expected to innovate methods to reduce waste.

Prevention is the first priority within an environmental management hierarchy that includes prevention, recycling, treatment, and disposal or release. Pollution prevention, however, requires a cultural change, one which encourages more anticipation and internalizing of real environmental costs by those who may generate pollution (United States Environmental Protection Agency n.d. ).

Our responsibility is to utilize our knowledge to take actions that are protective of human health and the environment.

All of the publications retained in this journal issue deal with methods and techniques suggested to maintain sustainable environment with its various components through pollution prevention and treatment. They were selected from the presentations in the International Conference in Integrated Management of Environment (ICIME) congress that has been conceived as a special platform to exchange knowledge among researchers from the Euro-Mediterranean region. The main idea was to share the different approaches to counteract the negative impacts of pollutants on the environment and human health: recent achievements of researchers acting within the region for remediation, protection, and smart management of pollution problems. It is the duty of the region scientist to find solutions for the negative impacts of pollutants resulting from our activities. The ICIME conference was held in Sousse, Tunisia, from 25 to 28 September 2016 to serve this purpose. More than 250 participants attended this event to share new findings and discuss the potential applications of such new processes that can be turned out to viable technique for sustainable development. A good and transparent work of selection has been undertaken to choose researches to inclusion in this special issue. The intense and fruitful exchange between the attending researchers showed their common concern to address the problem of waste and remediation together. Few problems may be specific to a given region, but similarity in the diagnosis as well as the remediation approaches demonstrated that the scientific community is in charge of bringing solutions for sustainable environmental protection. So, we are really grateful for researchers who attended this edition of ICIME; congratulations to the authors with published papers. We are thankful for all authors who actively contribute to the success of the meeting, hoping that there will be other occasions to meet and exchange ideas and new scientific findings. Doctor Philippe Garages, editor-in-chief of the Environmental Sciences and Pollution Research, and the editorial team are acknowledged for their endless help during the review process of this special issue.

Special Issue is intended to improve our understanding of the current research and achievements in the broad field of sustainable environment and pollution prevention management. It includes, but is not limited to:

Environmental assessment

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Elleuch, B., Bouhamed, F., Elloussaief, M. et al. Environmental sustainability and pollution prevention. Environ Sci Pollut Res 25 , 18223–18225 (2018). https://doi.org/10.1007/s11356-017-0619-5

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ORIGINAL RESEARCH article

Green innovation practices and its impacts on environmental and organizational performance.

• 1 School of Management, Jiangsu University, Zhenjiang, China
• 2 Lahore Business School, University of Lahore, Lahore, Pakistan

This study aims to investigate the impact of stakeholders’ views on the practices of green innovation (GI), consequent effect on environmental and organizational performance (OP), and moderating influence of innovation orientation. A quantitative method was employed for the sample size of 515 responses. To accumulate the data from the respondents, convenient random sampling was used. Data were collected from manufacturing and services firms through a field survey by using a closed-ended questionnaire based in the Punjab province of Pakistan. The analysis was done using the structural equation model of the partial least square analysis method. Our findings proved a positive and significant link between stakeholders’ views on GI practices. A significant association has been found between GI practices and environmental and OP. The moderating effect was found to be negative but statistically significant. This research offers numerous contributions and provides decision-making insinuations.

Introduction

Resource limitations and environmental concerns have made sustainable operations of assets and environmental pollution one of the major global issues. The economy’s overall development may not go “hand in hand” with the reduction of pollution and sustainable management of resources ( Wang and Song, 2014 ). Building a sense of balance among high resource consumption and development of economy relics is a constant challenge that forces organizations to run-through eco-friendly professional deeds having high economic worth ( Chan et al., 2012 ). Many organizations are forced to adopt activities that generate and increase economic value ( Porter and Kramer, 2019 ).

The excessive use of non-renewable resources prompted by speedy economic development has hurt the atmosphere and elevated various environmental worries ( Atlin and Gibson, 2017 ). To preserve energy and lessen emissions of carbon, numerous countries have established agencies and regulations for environmental sustainability and its protections; examples comprise limitations on “chlorofluorocarbons, the sustainable development announcements of the Johannesburg world summit,” and limits on the usage of few hazardous materials “electrical and electronic equipment requirements, the European Union’s Restriction of Hazardous Substances Directive” ( Weng et al., 2015 , p. 4998). Such impositions of rule and regulations have drawn the attention of environmental supervisors ( Zhu and Sarkis, 2004 ; Claver et al., 2007 ); they also have the same outcome in varying the management and competition practices between the organizations ( Feng and Chen, 2018 ). To adhere to the new eco-friendly regulations, to have a positive branding image ( Chen, 2008a ; Hillestad et al., 2010 ), to improve their firms’ performance and to have a competitive advantage ( Claver et al., 2007 ; Rusinko, 2007 ), organizations have had to accept eco-friendly practices ( Afridi et al., 2020 ).

Numerous investigations examined factors altering green innovations (GI) practices, such as environmental regulations, ethics, legal systems, and supply chain ( Feng and Chen, 2018 ; Gao et al., 2018 ; El-Kassar and Singh, 2019 ; Seman et al., 2019 ). Studies have also examined an increase in awareness, the general public, and stakeholder pressure linked to green environmental issues ( Foo, 2018 ). Moreover, literature provides evidence of optimized pressure from society, customers, and government bodies to practice GI. However, the literature lacks findings on the relationship of stakeholders’ pressure [competitor’s pressure, government pressure, and employee conduct (EC)] about GI practices. The manufacturing sector faces higher stakeholder pressure due to possibly the highest waste-producing sector ( Chen, 2008b ; Chang, 2011 ). The single industry was studied for GI practices ( Cordano et al., 2010 ; Lin and Ho, 2011 ). This study fills the gap in investigating these constructs in the manufacturing and service industries to enrich existing GI practices and stakeholder pressure literature. Moreover, stakeholder pressure (customer) was examined for GI in third party logistic firms ( Chu et al., 2019 ), as well as in express companies ( Zhang et al., 2020 ), and in manufacturing firms ( Song et al., 2020 ). Those three studies were conducted in China’s context, which highlights the issue of conducting and focusing on the stakeholder pressure in the manufacturing and service industries of Pakistan being a developing economy in the initial stages of GI practices adoption ( Shahzad M. et al., 2020 ).

“Go-green” is an initiative mainly employed by firms to deal with eco-friendly problems. Approaches to attain green abilities and emerging eco-friendly practices have focused on attention and discussion in the management sciences’ discipline over the years ( Ullah, 2017 ). To ease the acceptance of GI, firms must consider the significant factors and precursors in their business entities ( Arfi et al., 2018 ). These comprise apprehensions of consumers ( Zhu et al., 2017 ), preferences of professionals and owners ( Huang et al., 2009 ), competency of suppliers and partners ( Chiou et al., 2011 ), government regulating authorities and their regulations ( Kammerer, 2009 ), and the environmental, technological, and organizational factors of GI practices ( Lin and Ho, 2011 ). Green technologies consist of GI practices (e.g., green product, process, managerial, and marketing innovation) and the execution of green human resource management practices (e.g., green training and development, administrative support and culture, recruitment and selection, compensation, and benefits). GI is a significant strategic enabler to acquire justifiable development, as it practices energy-saving, environment-protecting, waste-recycling, and pollution-preventing methods ( Albort-Morant et al., 2018 ). Furthermore, GI can be divided into green product, green marketing, green processes, and green management that are intended for eco-friendly environment, decreasing consumption of energy and increasing efficient use of the resource, control over pollution emission, and waste recycling, improving the performance of the organization and providing the pollution-free environment to society at large scale ( Seman et al., 2019 ).

Previous studies have witnessed some proofs of the impacts of numerous drivers such as corporate environmental ethics ( El-Kassar and Singh, 2019 ), environmental regulations ( Feng and Chen, 2018 ), the legal system ( Gao et al., 2018 ), and green supply chain management practices ( Seman et al., 2019 ) on GI practices. To date, some systematic and comprehensive investigations of the precursors and factors of GI have been performed. Foo (2018) proposed that the increase in awareness and pressure from the stakeholders and the general public have necessitated organizations to be more transparent in facing and handling green environmental issues of their supply base execution. Hence, it is critical to focus on stakeholders’ views in an organization on establishing and sustaining GI abilities and practices. Then executives of organizations are involved in examining the essential factors necessary for creating GI practices. Are there pressures from established institutions’ regulations and competitor’s critical factors of GI? How should firms have dealt with the concerns of both internal and external stakeholders?

Furthermore, previous studies have concentrated on the manufacturing sector as it is one of the most critical waste producers that upset the balance of an environment. With rising trepidations on global pollution, this industry is facing increasing pressures from customers, society, and governing agencies to save energy, resources, protect the eco-friendly environment and maintain its sustainability ( Chen, 2008b ; Chang, 2011 ) or on a single industry (e.g., Cordano et al., 2010 ; Lin and Ho, 2011 ). It would be beneficial to offer an all-purpose model to investigate issues about GI for both the service and manufacturing firms. Therefore, in this study, we borrowed help from the “stakeholder theory” ( Freeman, 2010 ) to aid in our investigation methodology. This theory has been utilized to get a comprehensive view of a particular organization to examine stakeholders’ influence (participants) on GI practices. To answer the stakeholders’ pressure, organizations should focus on an overall strategic plan that involves and satisfies both internal and external stakeholder groups ( Bryson, 2018 ).

Review of Literature

Stakeholder view (sv).

The word “stakeholders” was initially used by the “Stanford Research Institute” in 1963 and was defined as “those groups without whose support the organization would cease to exist” ( Friedman and Miles, 2006 ). While this concept was first brought into a “strategic discipline” in 1984 by Freeman (1984) , stakeholders were not only separate from shareholders but also involved in the decision-making process ( Donaldson and Preston, 1995 ; Mitchell et al., 1997 ). In an academic view, the “stakeholder theory” holds a unique perspective for the organizations and offers a diverse description of a firm’s structure and everyday actions ( Sulkowski et al., 2018 ). The stakeholder theory, founded on four indispensable grounds ( Jones and Wicks, 1999 ), first suggests that organizations have associations with several procedures, all of which are upset or pretentious by their results ( Laplume et al., 2008 ; Co and Barro, 2009 ). Second, such links are recognized in the firms’ procedures and results and their stakeholders’ firms’ views.

Third, stakeholders’ inherent value, and comforts cannot be permitted to override the safeties of others ( Clarkson, 1995 ; Co and Barro, 2009 ). Fourth, the decision making of the organizations is the central point ( Alrowwad et al., 2017 ). Stakeholder theory has been accepted for numerous ecological scholarships in that it has been active in persuading both company environmental sensitivity ( Crane and Livesey, 2017 ) and environmental policies ( Salem et al., 2018 ). Although the outcomes have been mixed, and the stakeholders’ views on ecological management have been unpredictable. For example, Jaaffar and Amran (2017) found that the organizations’ board of directors is involved in deciding eco-friendly strategies and policies while small business entities and proprietors decide GI ( Huang et al., 2009 ). In addition, in manufacturing organizations in Germany, stakeholders have affected the firms’ selections concerning ecological response forms ( Murillo-Luna et al., 2008 ), and they were confidently related with unproved GI ( Wagner, 2007 ); in contrast, the association among eco-friendly policies and stakeholders’ administration was not perfect in Belgian organizations ( Buysse and Verbeke, 2003 ). The review paper by Seman et al. (2018) concludes that the stakeholders’ views have a more considerable influence on GI practices.

Green Innovation (GI)

Works of GI are commonly divided into two types. The first describes GI as a firm’s abilities ( Gluch et al., 2009 ), whereas the second defines GI as an organization’s environmental practices ( Lin and Ho, 2008 ; Ho et al., 2009 ). When it comes to organizational practices, GI is described as “the hardware or software innovation related to green products or processes” ( Song and Yu, 2018 ); it is proposed that GI comprises management practices and technological advancements that expand the environmental and organizational performance (OP) and provide a competitive edge to the firms ( Rennings, 2000 ). Other researchers recommend that GI consists of unique or altered systems, processes, products, and practices that provide an advantage to the environment and subsidize firms’ sustainability ( Xie et al., 2019 ).

A recent study expresses GI as “the new or modified products and processes, including technology, managerial, and organizational innovations, which helps to sustain the surrounding environment” ( Ilvitskaya and Prihodko, 2018 ). Moreover, GI may refer to “a creative initiative that reduces negative environmental impacts or that yields environmental benefits as it creates value in the market” ( Chen et al., 2006 ). GI is divided into two kinds, such as “green product innovations” (providing new green products to consumers) and “green process inventions” or “greening” business procedures ( Tang et al., 2018 ). Furthermore, due to the growing customer-centered apprehensions concerning environmental protection, ecological management has become a critical part of many firms’ strategic policies and tactical plans ( Chiou et al., 2011 ; Khan et al., 2019 ).

Regulations related to an environment may lead toward a “win-win situation” ( Chan et al., 2018 ) since they can perform dual tasks, increase profits and lessen pollution; It is proposed that GI should be categorized distinctively from other innovative maneuvers since it harvests not only a spillover consequence for exploration and expansion efforts but also optimistic external possessions such as enlargements in the atmosphere ( Kammerer, 2009 ). A study by Feng et al. (2018) on the Chinese industry’s manufacturing firms has shown that internal and external environmental orientation is significantly associated with GI practices. The utilization of GI practices inside and outside the firms’ restrictions are vital for impacting both economic and ecological performance goals ( Khan and Qianli, 2017 ; Saeed et al., 2018 ). Moreover, Lee et al. (2018) found that stakeholders’ pressure, organizational support, and societal expectations were significant factors for the motivation to adopt GI practices and corporate environmental responsibility ( Shahzad F. et al., 2020 ). Moreover, the study of Fernando et al. (2019) showed that GI, regulation, supplier intervention, and technology have a strong influence on sustainable performance mediated by service innovation capabilities. The study by Famiyeh et al. (2018) also supported eco-friendly practices, showing that environmental management practices have direct and indirect positive effects on environmental performance. Xie et al. (2019) used green product innovation as a moderator for the green process innovation and OP, but the study did not find the supported results.

Proposed Framework and Hypothesis Development

Proposed framework.

This study involves the three dimensions of stakeholders’ view (e.g., competitor pressure, government pressure, and employees conduct) as independent variables. Organizational and environmental performance are used as dependent variables. Moreover, GI practices (e.g., green product and green process) are used as mediators, and the moderating role is performed by innovation orientation (IO). A total of six hypotheses have been suggested and showed in Figure 1 .

Figure 1. Conceptual model of the study.

Hypothesis Development

We followed “Freeman’s stakeholder framework” ( Freeman, 2010 ). We used three stakeholders’ dimensions to view the government’s and competitors’ pressure as external and employees’ conduct as internal stakeholders. However, there are various other dimensions, such as customer, community, and supplier pressure. This study also treats both aspects of stakeholder’s views as factors that are employing pressure on the organizations and motivating the firms to improve environmental practices. Identifying eco-friendly business practices are becoming critical elements as organizations are confronted with “both internal and external forces/pressures from environmental agencies, governmental regulations, stakeholders, competitors, customers and employees” ( Wang and Song, 2014 ). Singh and El-Kassar (2018) conclude that the stakeholders’ view (e.g., pressure by the government, competitors, employees, customers, society, and suppliers, respectively) positively influences the GI practices.

Competitors Pressure (CP)

Organizations generally act in response to the movements of rivals and the operating industry. When competitors accept or implement new eco-friendly practices, organizations in the same sector will feel overstretched to reconfigure the structures and policies ( Durand and Georgallis, 2018 ). In short, organizations need to be attentive to their competitor’s products/services, actions, and norms and regulations of the industry they are part of so that their innovation abilities are similar to others in the industry. For instance, organizations must be conscious of new energy-saving, waste-recycling, pollution-preventing methods, and changes in processes used for the implementation and paraphernalia that are accessible in the market. They are required to have an eye on the methods their competitors have adopted to lessen energy costs while restructuring process and reconfiguring their manufacturing facilities to overtake/perform equivalent to/better than their rivals. Thus, to endure competitive spots, organizations may emulate competitors’ environmental practices and actions, especially the front-runners in their industries ( Abrahamson and Rosenkopf, 1993 ). Singh and El-Kassar (2018) found a positive relationship between stakeholders’ views and GI practices. Furthermore, a study on 442 Chinese firms also confirmed that competitors’ pressure provides organizations with more significant incentives to adopt GI practices ( Cai and Li, 2018 ). In another study ( Yu, 2019 ), the results revealed that formal and informal environmental regulation and pressures have strong influences on food-making companies’ GI activities. Thus, hypothesis 1 is established:

H 1 : Competitor’s pressure has a significant impact on GI practices.

Governmental Pressures (GP)

Various scholarships have explored the association among regulatory rules and environmental practices and have proposed that governmental pressures (GP) is a crucial factor of external stakeholders ( He et al., 2018 ). Variations in regulations and implementation of these changes by the government disturb organizational activities concerning environmental management ( Yakubu, 2017 ). In particular, to compete internationally, organizations must keep an eye on both international and national laws to overcome any obstacle. The consistency of the rules and organizations’ insights into the severity of the regulations will define the degree to which firms essentially execute environmental prevention practices ( Bernauer et al., 2007 ). The appropriate governance mechanisms and structural design can successfully manage and supervise the association between nature and mankind ( Famiyeh et al., 2018 ). Moreover, Tirabeni et al. (2019) showed that organizations are reevaluating their manufacturing processes in response to “societal and governmental” pressures concerned with eco-friendly well-being. Furthermore, the degree to which the government enforces/supports the regulations has a substantial influence on the firms’ environmental strategies ( Lindell and Karagozoglu, 2001 ; Zeng et al., 2011 ), creating a significant task to examine. A study by Zhang et al. (2019) on 224 firms of the manufacturing industry found that institutional pressure significantly affects green supply chain management practices and business performance. In a study by Huang et al. (2016) , results show that customer and regulatory pressure encourage green response and increase performance. A survey by Fernando and Wah (2017) , based on Malaysian firms, concluded that compliance with government regulations impacts environmental performance. Hence, we suggest hypothesis 2:

H 2 : Governmental pressure has a significant impact on GI practices.

Employee Conduct (EC)

Top management identifies the significance of environmental prevention and their responsibility to impact strategic planning and long-term goals related to environmental management. Steady appreciation and consideration of environmental drivers by the management should produce improved innovation and overall performance. Additionally, an organization’s future direction of ecological practices/activities mostly depends on the top management’s commitment toward the utilization of green practices and whether the executives can motivate employees to actively contribute to environmental management ( Tang et al., 2018 ). The same circumstances exist between employees. In a business, workforces are often the originators of environmental practices ( Daily and Huang, 2001 ). Organizations will strain to achieve ecological goals if the personnel/workforce do not contribute to their policies and strategies ( Zhu et al., 2008 ). Thus, firms must arrange and offer workshops and training on environmental concerns, include suitable employees, and improve their obligation to eco-friendly practices ( Reinhardt, 1999 ). Yen and Yen (2012) investigate the inside drivers motivating organizations to utilize green activities such as the top management commitment and relationships with vendors. The authors found a direct association between the proposed constructs of the study.

Furthermore, Gholami et al. (2013) examined senior managers’ perceptions about situations and the significances of using green practices. They presented that green technology acceptance, top management attitude, and apprehension for potential concerns are significantly interrelated. Moreover, they found an optimistic connection between the adoption of green practices and overall performance. The results from Cao and Chen (2018) study show that when the top management’s awareness increases, the association between coercive policies and GI strategy becomes stronger. Soewarno et al. (2019) propose that executives are responsible for making GI strategies that have to be implemented by employees. Such innovation strategies positively influence GI if applied appropriately. Thus, we propose hypothesis 3:

H 3 : EC has a significant impact on GI practices.

Environmental Performance

In this study, we have assessed the firms’ overall performance into two types: environmental and organizational. Environmental performance (EP) can be defined as “the environmental impact of a company’s activities on the natural surroundings” ( Klassen and Whybark, 1999 ). OP includes numerous elements, both financial and non-financial (e.g., market share, reputation, sales volume, stakeholders satisfaction, etc.) ( Venkatraman and Ramanujam, 1986 ).

Environmental performance encompasses the inclusion of eco-friendly ingredients in products, less pollution, reduced carbon emissions and waste at the source, advancements in energy-savings, efficiency in utilization of resources, reduction in the use of environmentally hazardous elements, etc. ( Zhu et al., 2010 ). Related to long-term ecological impacts, an organization’s regulatory methods, processes, practices including pollution protection, as well as resource utilization and waste lessening, are more fruitful than “end-of-pipeline solutions” ( Sarkis and Cordeiro, 2001 ; De Giovanni, 2012 ; Khan et al., 2019 ). Previous scholarships proposed that advancement in the production process and efficiency will upsurge opportunities to advance environmental performance ( Montabon et al., 2007 ). Along with these, a study by Seman et al. (2019) on the 123-manufacturing industry showed that GI practices significantly improve environmental performance. Hence, we established hypothesis 4:

H 4 : GI practices have a significant impact on environmental performance.

Organizational Performance

Organizational performance can be assessed both “financially and non-financially” ( Gounaris et al., 2003 ). To control environmental costs, organizations raise their productivity by adopting GI practices ( de Burgos-Jiménez et al., 2013 ). Similarly, organizations can establish new markets and upsurge their market share by employing and adopting environmental activities and practices ( Berry and Rondinelli, 1998 ; Berrone et al., 2017 ). A long-term organization goal, advancement into non-monetary performance can be demonstrated by enlarged customer loyalty, newly joined customers, and an improved image and reputation of an organization ( Blazevic and Lievens, 2004 ). Chen (2008a) suggested that innovators in GI will gain the “first-mover advantage,” which indicates an improved firm image, higher product prices, competitive advantages, and new market opportunities. A study by Tang et al. (2018) shows that GI practices have positive effects on OP. Moreover, a study by Zhang and Walton (2017) on 83 New Zealand firms concludes that GI has a positive influence on the firms’ performance. Thus, hypothesis 5 is constructed:

Hypothesis 5: GI practices have a significant impact on OP.

This study used IO as a moderator. It tested its effect on the association among EC and GI practices because the variable is allied with organizations’ policy settings and culture, which primarily correlate to the firm’s employees.

Innovation Orientation

Innovation orientation is a strategic orientation that disturbs firms’ innovation practices and functions as a guiding standard for making strategy and enactment to increase an organization’s innovativeness ( Chen et al., 2011 ; Stock and Zacharias, 2011 ). It defines a firms’ “openness to new ideas, technologies, skills, resources, and administrative systems” ( Zhou et al., 2005 ) and a knowledge-sharing system that unites a learning viewpoint, strategic guidelines, and trans -functional acclimation within a firm to encourage innovation ( Siguaw et al., 2006 ). IO is a crucial factor in overwhelming competitors and advancing an organization’s capability to effectively execute new products, services, systems, and processes ( Oke, 2007 ). Organizations with a new innovative environment and management will motivate and encourage employees to commence innovative conduct ( Ramus, 2018 ). Thus, we assume that an IO can advance the association between EC and GI practices, as exemplified in hypothesis 6:

H 6 : IO significantly moderates EC on GI practices.

Research Methodology

Based on a review of the literature, we considered a structured closed-ended questionnaire with 7 s. The first section includes the demographical information of respondents. The second to seventh sections include the measurement items related to specific construct’s competitors’ pressure, governmental pressure; EC; IO; GI practices; environmental performance, and OP. To ensure the validity of the questionnaire and data, two pilot studies were conducted. After that step, we adopted a field survey on a large scale. All of the construct’s items were measured using “five-point Likert-type scales in which 1 = strongly disagree, 5 = strongly agree.”

Data Collection and Sample

Data were collected from January 2019 to July 2019 from the manufacturing and services firms of Punjab province in Pakistan that have adopted GI practices. Convenient random sampling techniques were adopted for selecting areas of the country. Most of the organizations are based in Lahore, Faisalabad, Sheikhupura, Gujranwala, and Multan. Data collected by field surveys targeted the population, including the executives of different departments such as marketing, human resource, productions, operations, and other functional managers. After the pilot study’s conduction, 550 questionnaires were distributed among the respondents, out of which 520 were filled and returned. This resulted in a response rate of 94.54% from a random sampling method for data collection. Five forms were removed from the analysis due to incomplete information, and the remaining 515 were used in the analysis.

Measures of the Constructs

This study adopted a quantitative research technique and a closed-ended questionnaire used for data collection. All of the variables were assessed with multiple-item scales. In total, 46 question items, mainly related to the constructs, were used. Competitor pressure was appraised by acclimating four items from preceding studies ( Christmann, 2004 ). GP were measured by four items scale adapted from the studies of Zeng et al. (2011) and Qi et al. (2010) . EC was measured by four items scale taken from Lindell and Karagozoglu (2001) studies and López-Gamero et al. (2008) . IO was measured by seven items scale gained from the studies of Hurley and Hult (1998) ; Zhou et al. (2005) , and Siguaw et al. (2006) . In this study, GI practices were measured by nine items scale taken from the study of Chiou et al. (2011) . OP measured by eight items scale adapted from the study of Blazevic and Lievens (2004) and Avlonitis et al. (2001) . Moreover, the environmental performance was measured by six items scale adapted from Lin (2013) studies.

Common Method Bias

We used Harman’s single factor test to check the issue of common method bias in the data. As per Harman’s methodology, if all the factors merged into factor analysis, and the first factor explains more than 50% of the data variance, there is an issue of common method bias. Therefore, we used the dimension reduction method in SPSS and merged all the factors into one factor using a rotation matrix. The first factor’s results explained 38.23% of the total variance, which is less than 50% of the variance. Thus, common method bias is not considered as the problem in this study.

Data Analysis and Results

This study used the partial least squares (PLS) procedure of structural equation modeling using Smart-PLS Version 3.0 to assess the research model. This procedure was designated due to the investigative nature of the study ( Hair et al., 2011 ). As recommended by Hair et al. (2013) , this research applied a two-step method for statistical analysis. In the first step, the measurement model was analyzed. In the second step, the structural relationships among the latent constructs were assessed. This tactic was used to conclude both the reliability and validity of the theoretical variables before the model’s structural relationship was tested. Furthermore, Smart-PLS’s main reason includes the extensive popularity and acceptability of its application ( Hair et al., 2012 ). It also includes comprehensive information about the variables ( Hair et al., 2011 ).

Sample Demographics

A sample of 515 employees represents the telecommunication sector population in China, and demographical representation was shown in Table 1 . 392 (76.1%) respondents are male, and the rest, 123 (23.9%) respondents are female. Also, 246 (47.8%) respondents fall in the range of 31–40 years, followed by 219 (42.5%) in 20–30 years. From the education perspective, 291 (56.5%) respondents have a master’s degree, followed by 216 (41.9%) with a graduation degree, and the remaining (1.6%) with higher than master degree education, respectively. Furthermore, 218 (42.3%) respondents have a job in the sales and marketing department, 209 (40.6%) selected “other options,” apart from the HR and finance department. As for work experience, 260 (50.5%) respondents have 5–10 years of experience, followed by 127 (24.7%) with 1–5 years and the rest (24.3%) with 11–15 years of experience, respectively. As mentioned in the table below, 168 (32.6%) respondents have a monthly income of more than 60,000 rupees. Out of 515 respondents, 333 (64.7%) are married, and the rest, 182 (35.3%), are single.

Table 1. Demographical information.

Measurement of Model

The partial least square method was used to measure the reliability and validity of the respective constructs. The constructs’ internal reliability was evaluated by “Cronbach’s Alpha (CA), and Composite reliability.” According to Gefen et al. (2000) and Hair et al. (2013) , CA should be greater than 0.7. Moreover, Hinton (2014) categorized four ranges of CA. First, if the value falls in the range of 0.9, it falls in the area of excellent reliability. Second, if it falls between 0.7 and 0.9, it will have high reliability. Third, if it is in the range of 0.5 to 0.7, it will fall into the moderate area. Fourth, if it is <0.5, it will be categorized as low. Table 2 shows that all of the variables have values (e.g., CP = 0.851; GP = 0.829; EC = 0.851; IO = 0.764; GIP = 0.829; EP = 0.799; and OP = 0.892) which fall into the range of high reliability. Furthermore, to evaluate the convergent validity, the average variance extracted (AVE) is used. Fornell and Larcker (1981) and Bagozzi and Yi (1988) propose that AVE’s value should be greater than 0.5. As per results found in the table, all the values of constructs (0.691; 0.654; 0.627; 0.585; 0.598; 0.651; and 0.650) satisfied the rule of thumb. Chin (1998) recommended that loadings have a value greater than 0.5 because it indicates the constructs’ reliability. The item’s value can be between 0.4 and 0.7, as the value is also used by Umrani et al. (2018) . Hence, all the loading values are found in the range of 0.477 to 0.894. Hence, it is proved that all the values satisfied the rule of thumb established by the scholars.

Table 2. Measurement model.

Two methods were used to evaluate the discriminant validity (e.g., used to measure either construct used in the study well defined). Each construct is pure and not any multicollinearity involved. The dependent variable was evaluated by considering the correlations between the measures of hypothetically intersecting variables) of the variables. First, it was ensured that the cross-loadings of indicators should be greater than any other opposing constructs ( Hair et al., 2012 ). Second, according to the criterion of Anderson and Gerbing (1988) and Fornell and Larcker (1981) , the “square root of AVE for each construct should exceed the inter-correlations of the construct with other model constructs” ( Table 3 ). Hence, both methods ensured the satisfaction of the results and validity. All the results found in the study meet satisfactory status.

Table 3. Discriminant validity coefficients.

Another essential technique of partial least square to assess the model’s validity and multicollinearity includes the Heterotrait–Monotrait ratio. According to Henseler et al. (2015) . HTMT is the ratio of trait correlation to within correlation. The belief that if the HTMT value is going to increase >0.9, it will lack the discriminant validity, as mentioned in Table 4 . Furthermore, it is considered one of the most crucial technique to measure the multicollinearity.

Table 4. Heterotrait – Monotrait (HTMT) ratio.

Structural Model

The table given below contains the values of the coefficient of determination. It shows the percentage change in the dependent variable incurred because of independent variables. Hair et al. (2010) defined it as the proportion determined by independent variables. In other words, it tells how much change in dependent variable incurs because of the independent variable. Table 5 shows three models. In the path – 1: R 2 of GI practice, have a positive coefficient 0.716, and adjusted R 2 0.713. It entails that 71.6% of changes in GIP incur because of all the independent variables. Path – 2 exhibited a 31.7% change in EP. While path – 3 showed a 31.6% change in OP incurred because of all the independent variables. According to Hair et al. (2011) and Henseler et al. (2015) , three values of the coefficient of determination, 0.75, 0.5, or 0.25, which are called substantial, moderate, or weak, respectively. If the co-efficient of determination falls within the range of 0.75 or greater, it will become significant. If it is between 0.25 and 0.75, it will become moderate. If it falls below 0.25, it will be considered weak. Hence, the study’s value, which is shown in the table underneath, falls in a moderate range.

Table 5. Analysis of R 2 .

Analysis and Discussion

The competitors’ pressure, governmental pressure, EC, and GI practices are concentrated on environmental and OP. The manufacturing and servicing industries of the country were examined, which account for greater than 70% contribution to the GDP of the country. A cohesive framework was developed under the investigation of theory, and it stated that the stakeholders’ dimensions have positive and significant effects on the GI practice, and which, in turn, has positive and significant impacts on environmental and OP.

In the study, six hypotheses were constructed. Among them, five were a direct hypothesis, and one was proposed for the moderation effect. As exhibited in Table 6 and Figure 2 , the first direct hypothesis H 1 related to the influence of competitor pressure on GI practices. The findings show that competitive pressure positively and significantly impacts GI practices with a coefficient value of 0.271, t -value 5.543 > 2, and p -value 0.000 < 0.05. The hypothesis results were found consistent with the study of El-Kassar and Singh (2019) . Moreover, we tested H 2 governmental pressure positively related to GI practices. The results indicate that governmental pressure positively and significantly impacts GI practices with a positive coefficient value of 0.123, t -value 4.598 > 2, and p -value 0.000 < 0.05. The second direct hypothesis H 2 , won the vote of support and was consistent with the results from a previous study of Sezen and Çankaya (2013) and Fernando and Wah (2017) . Our third hypothesis, H3, is associated with EC and GI practices. The output illustrates that EC positively influenced GI practices with coefficient value of 0.185, t -value 4.368 > 2, and p -value 0.000 < 0.05. Hypothesis results were found consistent with the study of Yen and Yen (2012) , Gholami et al. (2013) , and Soewarno et al. (2019) .

Table 6. Path coefficients and hypothesis testing.

Figure 2. Structural model of the study.

Furthermore, we discussed the H 4 the direct effect of GI practices on OP. The findings show that GI practices positively and significantly affect OP with a positive coefficient value of 0.563, t -value 14.653 > 2, and p -value 0.000 < 0.05. Hypothesis results were consistent with the previous study of Seman et al. (2019) . Besides, we tested the direct effect of GI practices on environmental performance. We found that GI practices positively related to environmental performance with a positive coefficient of 0.562, t -value 16.15 > 2, and p -value 0.000 < 0.05. The hypothesis was supported and consistent with the studies of Zhang and Walton (2017) and Tang et al. (2018) . Finally, the sixth hypothesis H 6 was constructed for moderation interaction effects, and its results were found statistically significant with a negative coefficient value of −0.063, t -value 3.137 > 2, and p -value 0.000 < 0.05. In conclusion, the results of all direct hypotheses were found with a positive path coefficient and statistically significant with a t -value > 2 and p -value < 0.05 and the interaction graph presented in Figure 3 . However, the moderation hypothesis was found statistically significant, with a negative coefficient value. Therefore, it is proven that all the variables used in the study affect GI practices and the firms’ overall performance.

Figure 3. Interaction graph EC × IO and GIP.

Conclusion and Implications

“Go green” has been forcing internationally dynamic organizations to improve their green competencies endlessly, execute GI practices to prevent the environment from degrading further, and advance overall firms’ performance. Therefore, this study aims to identify the key factors affecting on the GI practices and its impact on OP from stakeholders’ perspectives. From the results, it is concluded that competitive pressure has a positive and significant impact on GI practices ( Abrahamson and Rosenkopf, 1993 ; Cai and Li, 2018 ; Durand and Georgallis, 2018 ; Singh and El-Kassar, 2018 ; Yu, 2019 ) as well as that governmental pressure has a positive and significant impact on GI practices ( Lindell and Karagozoglu, 2001 ; Bernauer et al., 2007 ; Zeng et al., 2011 ; Huang et al., 2016 ; Fernando and Wah, 2017 ; Yakubu, 2017 ; Famiyeh et al., 2018 ; He et al., 2018 ; Tirabeni et al., 2019 ; Zhang et al., 2019 ). Furthermore, it can be seen from our results that employee’s conduct is positively influenced by GI practices ( Reinhardt, 1999 ; Daily and Huang, 2001 ; Zhu et al., 2008 ; Yen and Yen, 2012 ; Gholami et al., 2013 ; Cao and Chen, 2018 ; Tang et al., 2018 ; Soewarno et al., 2019 ). Also, our results conclude that GI practices have a positive and significant effect on OP ( Berry and Rondinelli, 1998 ; Gounaris et al., 2003 ; Blazevic and Lievens, 2004 ; Chen, 2008a ; de Burgos-Jiménez et al., 2013 ; Berrone et al., 2017 ; Zhang and Walton, 2017 ; Tang et al., 2018 ). The findings of the study suggest that GI practices positively related to environmental performance. From the findings, it is also concluded that the moderation effect of IO was found statistically significant but with a negative coefficient value. The study also describes significant implications and suggestions to the managers and policymakers.

Implications

The present study delivers numerous researches “contributions and managerial implications.” First, this study presented that GI practices disturb not only EP but also OP. GI should be seen not only as responsive contentment of management requirements but as a pre-emptive exercise to advance a competitive advantage and the firm’s performance ( de Burgos-Jiménez et al., 2013 ). This pragmatic sign proposes that when organizations generously emphasize GI practices, they can promote both “financial and non-financial” performance. Top management executives can play a crucial role in carrying the significance of GI to all stakeholders. Second, both industrial and service organizations were investigated in the model. The data collected from both the sectors/industries showed no difference, and the results were the same. “Go green” is a significant issue for both divisions. GI practices need to be endlessly accepted in the product, process, marketing, management innovation, or all, regardless of industry. Finally, this study showed a statistically significant moderation effect of IO on EC concerning GI practices. However, we propose that the top management or executives accentuate innovation and inventiveness in their firm’s culture. The effort to raise the constituents of innovation is critical to the existence and sustainability of firms.

Limitations and Further Research

Although this research study delivers valuable intuitions, some limitations should fuel further investigations. First, the study was conducted in Pakistan, which only included significant areas of the country; small cities were ignored in the research. Second, an executive’s insights into GI practices and consequences are stranded in specific-industry norms. However, to focus on the conclusions’ larger generalizability, we invite scholars to replicate our study but in diverse perspectives and countries. Future studies should include other dimensions of the stakeholders’ view with the mediation of market innovation and management innovation. HR practices can also moderate the relationship between stakeholders’ views and GI practices. Last, the mediation effects need to be explored further.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

This study was carried out in accordance with the recommendations of the Ethical Principles of Psychologists and Code of Conduct of the American Psychological Association (APA). All participants gave written consent in accordance with the Declaration of Helsinki. The studies involving human participants were reviewed and approved by the Ethics Committee of the Lahore School of Business, University of Lahore, Pakistan. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

MK, HW, and DA: the provision of materials (i.e., questionnaires) and principal manuscript writing. MM, FS, and FA: data collection and manuscript revision and proofreading. MK and HW: data analysis plan. FS and FA: data analysis. All authors contributed to definition of research objectives, models, and hypotheses and approved the final version of the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Descriptive statistics.

Keywords : innovation orientation, competitor pressure, employees’ conduct, green innovation, environmental performance, organizational performance

Citation: Wang H, Khan MAS, Anwar F, Shahzad F, Adu D and Murad M (2021) Green Innovation Practices and Its Impacts on Environmental and Organizational Performance. Front. Psychol. 11:553625. doi: 10.3389/fpsyg.2020.553625

Received: 19 April 2020; Accepted: 03 November 2020; Published: 18 January 2021.

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*Correspondence: Muhammad Aamir Shafique Khan, [email protected] ; Farooq Anwar, [email protected]

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