Land-Use Change Impacts from Sustainable Hydropower Production in EU28 Region: An Empirical Analysis

Abstract

Under the current European Union (EU) constitution approved in May 2018, EU countries ought to guarantee that estimated greenhouse-gas releases from land use, land-use change, or forestry are entirely compensated by an equivalent accounted removal of carbon dioxide (CO₂) from the air during the period between 2021 and 2030. This study investigates the effect of sustainable hydropower production on land-use change in the European Union (EU28) region countries during 1990–2018, using the fully modified ordinary least squares (FMOLS). The results revealed that land-use change incline with an increase in hydropower energy production. In addition, economic growth, carbon dioxide emissions, and population density are found to be increasing land-use changes, while institutional quality is found to be decreasing land-use change significantly. The finding implies that land-use change in EU28 region countries can be significantly increased by mounting the amount of hydropower energy production to achieve Energy Union aims by 2030. This will finally be spread to combat climate change and environmental pollution. The findings are considered robust as they were checked with DOLS and pooled OLS. The research suggests that the EU28 countries pay attention to the share of hydropower in their renewable energy combination to minimize carbon releases. Politicians and investors in the EU28 region ought to invest further in the efficiency and sustainability of hydropower generation to increase its production and accessibility without further degradation of forest and agricultural conditions. The authorities of the EU28 region should emphasize on efficiency and sustainability of hydropower energy with land-use management to achieve the international commitments for climate, biodiversity, and sustainable development, reduce dependence on fossil fuel, and energy insecurity.

1. Introduction

1.1. Hydropower Background

To meet the European Union (EU) environmental sustainability aim by 2050 and the set target of a minimum of 55% mitigation in greenhouse-gas (GHG) releases by 2030, the European Commission (EC) is suggesting to amend the legislation on the implication of GHG releases and elimination from land use, land-use change, and forestry (LULUCF). The EC has issued an initial evaluation and started an open public conference on the amendment of the LULICF. The LULUCF legislation executes the treaty between EU countries in 2014 that all industries must engage in the EU’s 2030 releases mitigation objective, including the land-use industry. It is also aligned with the Paris International Agreement, which refers to the main contribution of the land-use industry in achieving the long-run environmental pollution and climate change objectives. The legislation designs an obligation for each EU country to assure that estimated releases from LULUCF are balanced by at least an equivalent accounted removal of carbon dioxide (CO₂) from the air through activities in the industry. This is recognized as the “no debit” principle. The recent principles give the EU countries a structure to encourage more environmental conservation land use, without major new limitations or constraints on individual actors.

The EC unveiled its proposal for a climate-neutral Europe by 2050 at the end of 2018, with a pledge under its Climate Change Innovation Fund to invest more than 10 billion EUR in innovative renewable energy from 2020. The goal of the Innovation Fund is to encourage innovative low-carbon technologies and procedures, including the production and storage of renewable energy. By growing and encouraging the use of modern and renewable energy, the EU countries have committed to cutting GHG emissions by 29 percent by 2030 [1]. As various renewables sources continue to grow rapidly, Europe is increasingly recognizing hydropower for its versatile services to preserve a stable, cost-effective, and sustainable energy supply. Europe added an estimated 2.3 gigawatts (GW) and 2.2 GW in 2017 and 2018, respectively, including pumped storage of 384 megawatts (MW) installed capacity in 2018, taking the total to 252 GW, including pumped storage of 57.4 GW. Hydropower is the largest sector of renewable electricity in the EU and already contributes to 40% of all renewable electricity generation in Europe (see Figure 1). It can contribute to achieving the aims of the Energy Union, in particular, to provide 20% of total renewable energy consumption by 2020 and at least 27% by 2030 [1].

Land-use change is often considered to be a primary driver for changes in biodiversity and ecosystems. In recent years, some of the most important land-use changes have included the following: a decline in agricultural land use, an increase in urban areas, and a gradual increase in forest land areas. The development of infrastructure has led to Europe’s landscape being increasingly broken up into small pieces during the period between 1990 and 2018 (see Figure 1). This pattern of fragmentation has the potential to affect levels of biodiversity and could result in negative impacts on flora and fauna [1].

Hydropower, however, is not without its environmental implications, specifically when it comes to land that is submerged under reservoirs or operated power lines and roads that are constructed for a hydropower project. At first, some hydropower reservoirs can look natural, but they are human-influenced, and if the land has been flooded for their formation, this could have an effect on terrestrial habitats. One of the possible environmental implications of creating hydropower is the potential effect on biodiversity, such as the disruption of freshwater ecosystems, destruction of water quality, and land-use change. The construction of hydropower reservoirs contributes to the rise in pollution from the decomposition of organic matter that was either flooded by the reservoir or flushed into it [2]. In the absence of reliable information and data on the impact of hydropower, we decide to concentrate on land use as changes in land use are a key environmental issue.

Hydropower poses particular environmental concerns associated with the change of land use and patterns of river flow. The construction of reservoirs could create significant ecological transitions from aquatic and terrestrial ecosystems to a lentic environment but also result in changes in land-use, in the form of relocating communities and engaging in several production activities. Hydropower energy production requires a significant horizontal land area to build and operate hydropower plants and directly competes with other users of land such as agriculture or forestry. This rivalry may lead to direct or indirect pollution as a result of land-use changes and will increase food insecurity where there is already a dependence on imported agricultural products (see Figure 1). If hydropower and other renewable energy sources are to completely achieve the existing renewable energy targets, land-use change emissions per unit of electricity generation can be as high as 9% or 40% of gas-fired electricity emissions. Generally, emissions from land-use change will nullify about 3% to 9% of the decline in emissions achieved by balanced changes of the electricity system to renewable energy in the EU region [2].

Hydropower is an important resource, but when water is stored behind dams, the land tends to be flooded. River changes have an impact on upstream and downstream ecosystems. Although the land-use intensity (5–10 m²/MWhel) may be reduced, particularly when large systems are involved, it has the potential to increase to over 500 m²/MWhel in smaller plants, with a size similar to that of bioenergy crops [3]. Direct and indirect changes in land use, however, may have major climatic consequences, both because of GHG emissions and the modification of local microclimates following albedo and evapotranspiration changes. Habitat and land-use change degraded terrestrial and marine habitats and diminished habitat connectivity were the impacts of biodiversity [3].

Therefore, a crucial question is “does sustainable hydropower production across the EU28 countries affect the land-use change level?” Due to the massive sustainable production of hydropower energy to meet the National Renewable Energy Action Plan (NREAP’s) aims by 2020 and 2030, a further investigation is needed for the causal correlation between sustainability of hydropower production and environmental impacts. To this end, the current empirical study objective to explore the linkages between sustainable hydropower production and land-use change, considering different economic, social, and environmental indicators in a single multivariate framework for both developed and emerging members in the EU28 region from 1990 to 2018.

There are many contributions to this analysis. It advocates developing a clear understanding of the ties between the study variables for integrated policy choices on sustainable development in EU28 countries. The study helps the governments of the EU28 region in combating land-use change emissions by recognizing the role played by hydropower in land-use and land-use change emissions at regional and sub-regional levels. The survey using the study’s variables is the first of its kind for countries in the EU28 region. In addition to the land-use change of agriculture and forestry, the study considers hydropower production to see whether it complies with the requirements for sustainability at regional and sub-regional levels before formulating regulations for more integration of hydropower into the renewable energy combination. This study investigates whether sustainable land-use and land cover change (LUCC) strategies are required to mitigate the environmental impacts of these hydropower plants. The study focuses primarily on the production of hydropower but provided an opportunity to examine the broader environmental effects of hydropower plants. This study focuses on two perspectives of hydropower in the region: hydropower sustainability and land-use change ecology and explored the potential for integrated sustainable land-use change management structures to balance often competing for economic, political, and environmental interests.

The application of the novel cross-sectional data estimation method created by Kwiatkowski, et al. [4] and developed by Koçak and Şarkgüneşi [5] is another contribution to the current empirical study. The current empirical research uses the newly developed panel cointegration model, differing from the empirical time-series and cross-sectional data approaches used in earlier studies, such as the regularly used panel cointegration model of Koçak and Şarkgüneşi [5]. The adopted approach in this study addresses the problem of spurious regression as well as the complex nature of dominant panel cointegration models. Finally, this empirical study adds to the environmental sustainability literature by analyzing the prevailing relationship between the ecology of hydropower and land-use change in EU countries during the period from 1990 to 2018.

1.2. Empirical Survey

A growing number of studies on different kinds of renewables and nonrenewable energies have highlighted their impacts on land-use and land-use change emissions. For example, Dijkman and Benders [6] investigated the correlation between renewable fuels and land-use using energy densities, which states that the ratio of energy densities for renewable energy is the average energy density scenario with electricity from conventional energy having the highest energy output/input ratio in Europe. Likewise, Fthenakis and Kim [7] argued that, relative to traditional energy sources, renewable energy sources are often perceived to be scattered and difficult to accumulate, thereby requiring significant land resources, arguing that conventional electricity generation technologies also have secondary effects on land usage, including pollution and ecosystem disturbances of neighboring land and land division. Similarly, Horner and Clark [8], while referring to renewable energy technology and location-dependent parameters such as insolation, packing factor, performance, and power factor, explored the amount of land used for a given amount of utility-scale electricity generation in the renewable industry varies widely across studies, and thus all contribute to the overall variability of the energy intensity of land use. Several previous studies focused on the correlation between renewable energy on land use, such as ref. [9,10,11,12,13,14].

Bakken, et al. [15] considered a new paradigm to compare environmental impacts of small- and large-scale projects in hydropower and wind energy, suggesting that small-scale hydropower performs less favorably in all parameters except land occupation. Likewise, Frolova [16,17] explored the structural and social processes from which river and hydropower ecosystems have emerged, indicating that the institutional growth of the river landscape in Spain has been closely linked to the growing importance of environmental concerns. Similarly, Frolova, et al. [18], investigated the effects of hydropower on the landscape in Europe, concluding that in conjunction with evolving policy structures, rapid technological developments in hydropower development, and distribution face particular challenges during planning to avoid deterioration of landscape quality. Identically, Bohlen and Lewis [19], examined the economic impacts of hydropower dams on property residential values using a geographic information system. The study claimed that hydropower generating plants have major effects on property residential values and landscape. Many studies explored the relationship between hydropower plants and environmental destruction, such as ref. [20,21,22,23,24,25].

On the other hand, Ovando and Caparrós [26], surveyed the correlation between land-use change and carbon dioxide in the EU and claims that land-based alternatives are capable of contributing between 13% and 52% of the proposed European carbon emissions target by 2020. Moreover, implementing these alternatives will require that 8 to 30 percent of the EU25 agricultural land be forested or diverted at the same time. Similarly, Delzeit et al. [27], addressed the unanswered issue of how to measure indirect land-use shift emissions using models of the economic simulation. The study found that models of economic simulation involve high levels of uncertainty concerning the main parameters of the model. Similarly, ref. [28], investigated the effect on GHG emissions of land-use transition, claiming that the carbon dioxide rate increasing significantly with land-use change. In the same manner, Dumortier, et al. [29] explored the carbon emission sensitivity from indirect land-use change, arguing that the effect on carbon emissions of the expansion of land-use change is highly susceptible to model assumptions. Several previous studies have investigated the relationship between land-use change and carbon dioxide emission, such as ref. [30,31,32,33].

1.3. Theoretical Survey

Theoretically and methodologically, various approaches and techniques have been employed to investigate the correlation between the hydropower industry and ecological impacts. Siddiqi, et al. [34], for instance, carried out a multidimensional empirical study of the complete portfolio of hydropower, inspired by the need to consider the potential environmental impacts of hydropower. Similarly, Gracey and Verones [24] compared various hydrological models and explored how to adjust or merge the current life-cycle impact-assessment methodologies to enhance the assessment of hydropower development impacts on biodiversity. A benefit–cost study of a relicensing arrangement for two hydroelectric dams in Michigan was performed by Kotchen, et al. [21], exploring the impact of environmental restrictions on hydroelectric dams. While Lakhani, et al. [9] developed a structure that can be used to illustrate and measure the tradeoffs between cost and cost of life-cycle land-use (LCC), life-cycle land-use footprints (LUF), and consequent impacts of land use (LUI) through different energy systems implementation choices. Based on the earlier study ref. [31,35] combined a global computable general equilibrium model with a model of land-use change GHG emissions to quantify the parametric uncertainty of the combined modeling system’s ILUC GHG emission estimates induced by increased production of sustainable energy.

Locatelli, et al. [36] applied the conceptual paradigm where the variables of output, flow, and utility are ambiguous numbers derived from expert information and spatial data to map ecosystem service flows. The ecosystem service level obtained by the endpoint spatial unit depends on the performance of the upstream catchment area ecosystem services and the ability of the services to flow from the ecosystem to the target space unit. The study applied a quantitative assessment for the effect of hydroelectricity generation on land-use change policy, and land use can be compared in terms of energy production for hydroelectric power catchments, indicating that the forestation of catchments reduces water yield. Plevin, et al. [32] used a reduced-form model in another study to estimate the bounding range through which energy indirectly increases land-use change (ILUC) emissions, considering various distributions of probability for model parameters. A previous study [33], used a worldwide agricultural model to estimate carbon dioxide emissions from land-use change in the USA.

Interestingly, it seems that no previous study is assessing the impact of sustainable hydropower production on the EU region environment and natural resources. Moreover, no earlier research investigated the effect of hydropower industry factors on land-use change in both developed (EU15) and underdeveloped (EU13) European Union economies from 1990 to 2018 applying the panel cointegration approach. Considering the study information gap explains the cause behind this empirical research applying the fully modified ordinary least square (FMOLS), dummy ordinary least square (DOLS), and pooled ordinary least square (pooled OLS) approaches to evaluate the correlation between the sustainability of the hydropower industry and negative impact of land-use change in the EU28 region, EU15 developed countries, and EU13 underdeveloped countries.

2. Methodology and Data

2.1. Theoretical Background

Researches that frame land-use change might be segregated into two main groups: spatially explicit and aspatial. Spatially explicit conceptual framework are generated from the fields of geography and landscape environment and elaborate the position of changes in land use as a role of the behavior of landowners [37], or of different growth “standards”, such as simulation functions [38]. Aspatial models have been framed from the Von Thunen tradition, where the land-use method is elaborated by bid–rent functions that assign availability and interval to the markets as the main factors of land use [39]. Both models suggest that land users are reasonable investors who select land use appropriately. This empirical study assesses aspatial functions of land-use change in the EU28 region countries. This empirical research selects an aspatial function for the following: firstly, the authors are concerned with the magnitude of land-use change. This concentrate is selected to remedy issues related to the effect of land-use changes on CO₂ emissions [40], the loss of land’s natural resources linked with economic growth, and the relation between land-use change and local and foreign output markets. Moreover, a spatially explicit function is improbable to consider the ecological regulations and trade that drive land-use changes in the EU28 countries.

A significant point in the estimation of panel data is to consider a potential dependency among EU28 members. This is due to the level of economic integration that is very often so repeated that one member can be influenced by the economic shocks of other members. This might be highly strong, even typical, for the economic growth variable. Hydropower energy is no exception nor the institutional quality determinant. Grew concourse in hydropower energy and land-use policies certainly explains the observed members’ dependence concerning this determinant. Likely, EU28 nations governments could find a suitable governance framework that may elaborate in significant part dependence among EU28 members concerning institutional quality and land-use change. For this lattter reason, this study first examines cross-sectional dependency and country-specific heterogeneity.

2.2. Model and Data

Under the investigation with the purpose to analyze the relationship between hydropower (HP), institutional quality (IQ), carbon dioxide (CO₂), economic growth (GDP), and population density (PD) according to the investigations [5,40,41]. The authors used the quadratic function of dependence (Equation (1)), which is based on the EKC hypothesis:

E = F (Y, Y², Z)

(1)

where E is environmental degradation (land-use change), Y is output (hydropower output), and Z are the explanatory variables. Due to the necessity of considering the level of institutional quality as the GDP and population density on indicating the land-use change, the function (Equation (1)) for a panel study could be written as:

LUC_it = β₀ + β₁ HP_it + β₂ GDP_it + β₃ IQ_it + β₄ CO_2it + β₅ PD_it + ε_it

(2)

where the parameters β₁, β₂, β₃, β₄, and β₅ in Equation (2) are output elasticities, concerning various variables, while ε is the standard error. LUC is the land-use change proxied by agricultural land-use intensity (% of land area) for individual country i at time t. HP represents hydropower prime production in thousand tons of oil equivalent (TOE); GDP is the annual gross domestic product growth (%); IQ is the institutional quality representing governance (such as control of corruption, government effectiveness, voice and accountability, rule of law, political stability and regulatory quality); and CO₂ is the carbon dioxide emission level metric tons per capita. PD is the population density per square kilometer. We used data for the EU28 region from 1990–2018, obtained from the World Development Indicators and Eurostat (see

Table 1. Summary of variables.

Variable	Abbreviated	Data Source	Statistics/Sign	Unit
Land-Use Change	LUC	World Bank Datasets	Dependent Variable	Agriculture Land as % of Land area
Institutional Quality	IQ	World Bank Datasets	Significant/−	% of governance confidence
Hydropower Output	HP	Eurostat	Significant/+	Terajoule
Carbon Dioxide	CO2	Eurostat	Significant/+	Metric tons per capita
Economic Growth	GDP	World Bank Datasets	Significant/+	GDP growth annual %
Population Density	PD	World Bank Datasets	Significant/+	People Per sq.km

).

Firstly, to determine the stationarity of our data, we used [42,43] panel unit-root estimators that suggest homogeneity among cross-sections. The null hypothesis H₀: P_i = 1 of the panel unit root study is that all series contain unit roots, or all panels are nonstationary, based on numerous studies such as ref. [4,5,40]. While the alternative hypothesis H₁: P_i > 1 suggests stationarity:

$$\Delta {\mathrm{y}}_{\mathrm{i},\mathrm{t}} = {\mathsf{\alpha }}_{\mathrm{i}} + \mathsf{\delta }{\mathrm{y}}_{\mathrm{i},\mathrm{t}} + {{\displaystyle \sum }}_{\mathrm{L}=1}^{\mathrm{Pi}}{\mathsf{\theta }}_{\mathrm{i},\mathrm{L}} \Delta {\mathrm{y}}_{\mathrm{i},\mathrm{t}} + {\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\mathrm{m} = 1, 2, 3$$

(3)

where ε_it is uncorrelated across all countries, Δ is the first difference, Z^/_itZγ represents the parameters of individual countries, Δy_it and Δy_it−1 have individual estimates with Δy_it−L. L (L = 1, 2, 3, 4, …, P_i) therefore indicates the highest appropriate lag length specified by the values of information criteria.

Secondly, among panel cointegration estimators, the Pedroni assessment emerged as one of the most significant estimators. By applying unique parameters that may vary between individual members and cross-sectional interdependence, the Pedroni analysis takes heterogeneity into account [44,45]. This research applied a popular methodology to emphasize that if at least four test statistics yield proof of cointegration, the null assumption of no cointegration is rejected. To evaluate the long-term relationship between the dependent and independent variables in the panel, a panel cointegration test was applied. The panel cointegration methodology in Pedroni [46,47] evaluates the null hypothesis and the alternative hypothesis of cointegration:

Hypothesis 0 (H₀):

there is no cointegration correlation between all groups.

Hypothesis 1 (H₁):

there is a cointegration correlation between all groups.

2.3. Estimation Techniques

Several panel data cointegration regression estimators were used to evaluate the presence of a long-term relationship between the variables, including FMOLS, DOLS, and Pooled OLS. ref. [44,45] developed the FMOLS estimator in the following form of Equation (4).

Estimation of the coefficients of the panel cointegration in Equation (4) cut across all cross-sections, while the panel cointegration coefficient for the overall panel is obtained by computing the average resulting FMOLS coefficients for each coefficient. In this case, the econometric approach of the FMOLS is framed as:

$${\widehat{\mathsf{\beta }}}_{\mathrm{GFM}}^{*} = \frac{1}{N} {{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{N}}{\widehat{\mathsf{\beta }}}_{\mathrm{FM},\mathrm{i}}^{*}$$

(4)

where ${\widehat{\mathsf{\beta }}}_{\mathrm{GFM}}^{*}$ denotes the results of the group-mean panel FMOLS estimate given by the cross-sections that frames each i’th in the panel. For the statistical importance scale of the expanded interval parameter, the T statistic is computed as:

$${\mathrm{t}}_{{\widehat{\mathsf{\beta }}}_{\mathrm{GFM}}^{*}} = \frac{1}{N} {{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{N}}{\mathrm{t}}_{{\widehat{\mathsf{\beta }}}_{\mathrm{FM},\mathrm{i}}^{*}}$$

(5)

The cointegration coefficient value for the whole is represented by the ${\mathrm{t}}_{{\widehat{\mathsf{\beta }}}_{\mathrm{FM},\mathrm{i}}^{*}}$ statistic in Equation (5).

3. Results and Conclusions

3.1. Results

Preliminary analyses, including descriptive statistics, and a relationship matrix to assure eligibility and convenience of the series for the study were performed before estimating the panel cointegration model. The results of the descriptive statistics and the correlation matrix are shown in

Table 2. Descriptive statistics.

Variable	Observations	Mean	Std. Dev.	Min	Max
LUC	644	3.309	0.420	3.300	3.320
HP	644	4.225	0.837	2.360	4.360
GDP	644	4.379	0.395	3.130	5.250
CO2	644	0.915	0.239	0.430	1.820
IQ	644	1.867	0.699	1.480	1.980
PD	644	2.037	0.385	1.230	3.180

and

Table 3. Correlation matrix.

Variables	HP	CO2	GDP	IQ	PD
HP	1.000
CO2	−0.105	1.000
GDP	0.015	0.300	1.000
IQ	0.048	0.206	0.645	1.000
PD	−0.105	−0.018	−0.034	0.124	1000

, respectively. The findings of the summary statistics suggest that the series is well spaced and normally distributed, while correlation analysis shows that there is a high correlation among the independent variable. This is because all values are below [48] rule of thumb or the benchmark value of 0.8 for multicollinearity.

The key step in this study is to ensure the integration order of our series; that is, there must be first-order I (1) integration of the dependent variable. However, the case is different for the independent variable as not all independent variables are required to be stationary. This study applies the Levin, Lin, and Chu (LLC) and Im, Pesaran, and Shin, (IPS) unit root tests [42], and the findings are shown in

Table 4. Panel unit root test results for the EU28 region in 1990–2018.

Variable	Difference		First Difference
	LLC	IPS	LLC	IPS
LUC	−15.709 *** (0.000)	−15.008 *** (0.000)	−13.636 *** (0.000)	−14.042 *** (0.000)
HP	−11.965 *** (0.000)	−18.570 *** (0.000)	−8.752 *** (0.000)	−16.937 *** (0.000)
CO2	−40.823 *** (0.000)	−30.431 *** (0.000)	−33.813 *** (0.000)	−28.629 *** (0.000)
GDP	−80.363 *** (0.000)	−31.578 *** (0.000)	−60.136 *** (0.000)	−27.427 *** (0.000)
IQ	−21.518 *** (0.000)	−21.871 *** (0.000)	−17.382 *** (0.000)	−20.195 *** (0.000)
PD	−20.926 *** (0.000)	−24.181 *** (0.000)	−18.929 *** (0.000)	−22.854 *** (0.000)

Remark: *** refer importance at the 1%, scale; Levin, Lin, and Chu test (LLC), and Im, Pesaran, and Shin W-stat test (IPS).

indicate that the null hypothesis of the unit root for all study variables cannot be rejected at level one but can however be rejected at the first difference. The variable under review shows the first-order integration. The dynamic panel methods, such as FMOLS, are therefore sufficient and efficient for this study.

Having determined the order in which the variables are cointegrated, we then checked the presence of a long-run relationship between the variables using the Pedroni residual cointegration test, while the Kao residual cointegration test was applied to validate the results. The findings of the applied tests are displayed in

Table 5. Panel cointegration test results for the EU28 region during 1990–2018.

Dependent Variable: Land-Use Change
Table Header	Without Trend	With Trend
Pedroni Residual Cointegration Test
Alternative hypothesis: common AR coefficients. (within dimension):
Panel v-Statistic	−2.875 (1.000)	0.130 (0.448)
Panel rho-Statistic	−0.311 (0.377)	−0.558 (0.773)
Panel PP-Statistic	−8.385 *** (0.000)	−5.453 *** (0.000)
Panel ADF-Statistic	−4.102 ** (0.000)	2.088 *** (0.000)
Alternative hypothesis: common AR coefficients. (between dimension)
Group rho-Statistic	0.035	0.514
Group PP-Statistic	−9.802 ***	(0.000)
Group ADF-Statistic	−4.650 ***	(0.000)
KAO Residual Cointegration Test
ADF	−2.578 ***	(0.005)

Remark: ***, ** refer importance at the 1%, 5%, and 10% scales, respectively; values in parentheses are p-values.

. Pedroni [46] proposed the within and between dimensions as the two types of residual tests to be considered. The within dimension involves the estimates of four sub-tests—panel-v, panel-rho, PP, and ADF statistics, whilst there are three subsets in the between dimensions namely: group rho, PP, and the ADF statistics. The null hypothesis of the test suggests that there is no cointegration among the variables. Hence, the rejection of the null hypothesis means the presence of a long-run relationship among the variables. Results in Table 5 indicate that four of the seven Pedroni test statistics are significant. This means that there is a long-run relationship between the variables. Hence, we reject the null hypothesis of no cointegration. This conclusion follows the recommendation of ref. [46,47], which emphasized the significance of the panel ADF and group ADF statistics to conclude that there is cointegration. In addition, the results were validated with the Kao residual cointegration test results (see Table 5).

Table 6. Summary of panel regression model 1 for the EU28 region from 1990–2018.

Model 1. Panel Data Analysis Estimation for EU28 Region 1990–2018
Dependent Variable: Land-Use Change
Long-Run Coefficient	DOLS		FMOLS		Pooled OLS
	Coefficient	Std. Error	Coefficient	Std. Error	Coefficient	Std. Error
HP	0.740 ***	0.018	0.429 ***	0.051	0.421 ***	0.027
GDP	0.038 **	0.017	0.192 ***	0.094	0.117 ***	0.026
CO2	0.149 ***	0.018	0.180 ***	0.050	0.223 ***	0.044
IQ	−0.093	0.081	−1.003 ***	0.057	−1.049 ***	0.108
PD	−0.079 ***	0.076	0.034 ***	0.035	0.058	0.038
R-sqaured	0.898		0.931		0.753
Adjusted R-squared	0.812		0.930		0.846
S.E. of regression	0.082		0.094		0.065
Long-run variance	0.012		0.009		0.045

Note: ***, ** indicate significance at the 1%, 5%, and 10% levels, respectively; values in parentheses are p-values.

displays the results of the FMOLS model, the DOLS, and the pooled OLS. However, this analysis concentrates on the FMOLS results, whereas the results from DOLS and pooled OLS are adopted as robustness tests. The result of the FMOLS indicates that hydropower production has a significant positive relationship with changes in land use. This implies that an increase in hydropower production mounting land-use change in the EU28 region. Precisely, a 1% increase in hydropower production will lead to a 0.429% incline in land-use change. This finding is consistent with ref. [15,16]. The result indicates that EU28 region member countries are faced with the challenge of achieving their stipulated renewable energy target by expanding hydropower energy production. The decline in land-use change craved for by the EU28 region members overall hardly can be met by excessing the amount of hydropower output in manufacturing activities.

The result further shows an important positive influence of economic outgrowth on land-use change, implying that a percentage increase in economic growth would increase land-use change by 0.192%. This outcome is following earlier studies by ref. [49,50,51]. This generally implies that additional growth of the economy accelerates land-use change in the EU28 region. Population density also has a significant positive coefficient. Specifically, a percentage increase in population density increases land-use change by 0.034%, suggesting that increasing population density leads to more land-use change. This is in line with [52,53]. By implication, land-use change in the EU28 region increases following enhanced urbanization activities.

CO₂ has a significant positive coefficient, which implies that a rise in CO₂ by 1% achieves an increase in land-use change by 0.180%. This outcome lends empirical to ref. [26,27]. It suggests that land-use change in the EU28 region rise with an increase in CO₂ emissions. As foreseen, the FMOLS model result explains that institutional quality has an important negative impact on the land-use change at the 1% statistical scale. Exactly a 1% growth in institutional quality reduces the land-use change by 1.003%. This is aligned with the prior research of ref. [54,55], which refers that institutional quality contributes to a reduction in land-use change. This suggests that the improvement of the institutional efficiency and compliance with outstanding criterion achievements ought to help to mitigate the land-use change in the EU28 region.

In

Table 7. Summary of panel regression model 2 for EU15 developed countries from 1990–2018.

Model 2. Panel Data Analysis Estimation for EU15 Developed Countries 1990–2018
Dependent Variable: Land-use Change
Long-Run Coefficient	DOLS		FMOLS		Pooled OLS
	Coefficient	Std. Error	Coefficient	Std. Error	Coefficient	Std. Error
HP	0.674 ***	0.036	0.335 ***	0.031	0.328 ***	0.019
GDP	0.100 ***	0.031	0.050 ***	0.095	0.036 *	0.021
CO2	−0.168 ***	0.041	0.016 ***	0.083	0.023	0.023
IQ	−0.359 ***	0.125	−1.032 ***	0.053	−1.046 ***	0.066
PD	0.026 ***	0.059	0.051 ***	0.036	0.048 ***	0.077
R-squared	0.922		0.859		0.754
Adjusted R-squared	0.929		0.895		0.754
S.E. of regression	0.078		0.026		0.061
Long-run variance	0.012		0.022		0.011

Note: ***, * indicate significance at the 1% and 10% levels respectively; values in parentheses are p-values.

, the FMOLS result shows that hydropower production is positively and significantly associated with land-use change. This implies that expanding hydropower production further escalates land-use change in the EU28 region. Precisely a 1% increase in hydropower production leads to a 0.335% incline in land-use change. This finding is consistent with [22,23]. The result indicates that EU15 developed members have the barrier of implementing their Energy Union aims by reducing the quantity of hydropower output produced. The incline in the land-use change required by the EU15 developed members cannot be achieved by amounting to the production of hydropower output.

Population density also appears to be positive and significant. Precisely, a percentage increase in population density results in a 0.051% rise in land-use change. This means that more population density results in greater land-use change. This assertion is in tandem with Bonilla-Moheno, et al. [56]. By implication, land-use change in the EU15 developed countries increase as they expand the development of rural areas. On the other hand, a previous study by Wellmann, et al. [57] suggests that green growth identify the relation between population density, land use, and vegetation such as the case study city Berlin, Germany, which developed into a city that is both gaining in vegetation (greening) and population (growing) in recent years. Pathways to achieve a greening and growing scenario in a compact city include green roofs, brownfield, and industrial revitalization, and bioswales in predominantly green city districts.

Economic growth shows a significant positive impact on land-use change. This reveals an increase in economic growth facilitates land-use change by 0.050%. This outcome is following earlier studies [58,59]. It implies that more growth of the economy accelerates land-use change in the EU15 developed members.

As predicted, the FMOLS estimator result shows that institutional quality is statistically important and impacts land-use change negatives. In particular, a 1% growth in institutional quality reduces the land-use change by 1.032%. This result is identical to prior research, for example, ref. [60,61], which refer to that institutional quality motivates a reduction in land-use change. This suggests that the growth of good governance and alignment with proper effective enforcement ought to lead to a reduction in the land-use change in EU15 developed countries. Carbon dioxide has a significant positive, and it implies that a rise in the level of carbon dioxide by 1% leads to the rate of land-use change by 0.016%. This outcome validates previous studies [30,31]. It suggests that land-use change in the EU15 developed members incline with a rise in CO₂ emissions.

In

Table 8. Summary of panel regression model 3 for underdeveloped countries from 1990–2018.

Model 3. Panel Data Analysis Estimation for Underdeveloped Countries 1990–2018
Dependent Variable: Land-Use Change
Long-Run Coefficient	DOLS		FMOLS		Pooled OLS
	Coefficient	Std. Error	Coefficient	Std. Error	Coefficient	Std. Error
HP	0.769 ***	0.037	0.756 ***	0.034	0.752 ***	0.029
GDP	0.039 ***	0.080	0.011 ***	0.051	0.020 *	0.010
CO2	−0.047 ***	0.010	0.039 ***	0.053	−0.035 **	0.014
IQ	−0.021 **	0.093	−0.050 ***	0.012	−0.067 ***	0.083
PD	−0.048 **	0.082	0.070 ***	0.057	−0.026 **	0.012
R-sqaured	0.986		0.965		0.951
Adjusted R-squared	0.954		0.987		0.753
S.E. of regression	0.054		0.026		0.025
Long-run variance	0.085		0.042		0.024

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively; values in parentheses are p-values.

, the FMOLS result shows that hydropower production is positively and significantly associated with land-use change at a 1% level. By implication, an expansion in hydropower production mounting land-use change in the EU13 underdeveloped members. More exactly, a 1% growth in hydropower production gives a 0.756% increase in land-use change. This result is in line with [24,25]. This suggests that EU13 underdeveloped countries have the obstacle of performing their NREAPs aims by increasing hydropower energy produced. The rise in land-use change claimed by the EU13 underdeveloped countries would not be somewhat attained merely by increasing the production of hydropower energy.

Economic growth significant positive effect on land-use change. The result suggests that a percentage increase in economic growth would influence land-use change by 0.011%. This outcome is in tandem with earlier studies [62,63]. It implies that more growth of the economy accelerates land-use change in the EU13 underdeveloped countries. As with EU15 developed countries, population density is also positive and significant in EU13 developing countries. Precisely a percentage increase in population density results in a 0.070% rise in land-use change. This means that more population density results in greater land-use change. This is following ref. [64,65,66]. By implication, land-use change in the EU13 underdeveloped countries increase as they expand development and engage in more human activities.

Carbon dioxide (CO₂) emissions have a significant coefficient, implying that a 1% increase in carbon dioxide emissions leads to a rise in land-use change by about 0.04%. This finding validates and lends empirical support to ref. [32,33]. It suggests that land-use change in the EU13 underdeveloped countries rise with an increase in CO₂ emissions. On the other hand, the prior study [67] refers to that large-scale deployment of bioenergy has been identified as a key long-term option to keep CO₂ emissions limited over the twenty-first century. However, the rate of biomass density plays a significant role in land-use change.

As anticipated, the FMOLS estimator outcomes show that institutional quality has an important negative influence on the land-use change at the 1% scale. In particular, a 1% improvement in institutional quality declines the land-use change by 0.050%. This outcome is consistent with prior research [68,69,70] which points out that institutional quality encourages a decrease in land-use change. This suggests that the growth of government effectiveness and alignment with highly efficient applications could lead to a decline in the land-use change in EU13 underdeveloped countries.

3.2. Discussion

Although both estimations exhibit high adjusted R-squared values for DOLS and FMOLS, respectively, the determination coefficient R-squared for different estimation models of the impact of HP, GDP, CO₂, IQ, and PD on LUC was at the highest level.

The DOLS and the OLS were applied to check the robustness of the estimates obtained from the FMOLS. The robustness check shows that the FMOLS results are robust and therefore suitable for inference as it can closely be observed that the results of both the DOLS and the OLS have similar coefficients and levels of significance as the results from the FMOLS, although with a small difference in the level of significance. Generally, the FMOLS estimates can be considered robust and free from common dynamic panel problems such as endogeneity and serial correlation issues. To examine the influence of sustainable hydropower production on land-use change in the EU28 region, the region was divided into two groups according to its achievement levels: EU15 emerged and EU13 emerging members (please refer to Appendix A

Table A1. List of the EU28 region member countries.

		European Union (EU28) Region
Developed Countries			Underdeveloped Countries
Member Countries	Year		Member Countries	Year
Austria	1995		Bulgaria	2007
Belgium	1958		Croatia	2013
Denmark	1973		Cyprus	2004
Finland	1995		Czech	2004
France	1958		Estonia	2004
Germany	1958		Hungary	2004
Greece	1981		Latvia	2004
Ireland	1973		Lithuania	2004
Italy	1958		Malta	2004
Luxemburg	1958		Poland	2004
Netherlands	1958		Romania	2007
Portugal	1986		Slovakia	2004
Spain	1986		Slovenia	2004
Sweden	1995
United Kingdom	1973

Source: European Union Official Website (www.Europa.eu (accessed on 18 December 2020)).

).

The United Kingdom, France, Italy, Spain, Poland, and Germany are the six top hydropower producers in the EU28 region. The estimated outcome of the effect of sustainable hydropower development on land-use changes in developed EU15 countries is shown in Table 7. On the other hand, Table 8 shows the effects of sustainable hydropower development on changes in land use in underdeveloped EU13 countries, and Table 8 shows the results. Table 7 and Table 8 demonstrate that sustainable hydropower has a significant impact on land-use change, both directly and/or indirectly. Furthermore, the findings suggest that the positive impact of sustainable hydropower on land-use change is greater in the emerging countries EU13 than in the emerged EU15. Specifically, the impact magnitudes for EU13 and EU15 countries are 0.756 and 0.335, respectively. This implies that a remarkable increase in land-use change releases may occur in EU13 underdeveloped members by producing hydropower energy than in EU15 developed countries.

Likewise, the results show that the positive impact of carbon dioxide and population density on land-use change is greater in the EU13 members than in the EU15 members. In particular, the impact magnitudes are 0.039 and 0.070 for EU13 and 0.016 and 0.051 for EU15 members, respectively. This indicates that a remarkable transformation in the natural landscape can occur in EU13 underdeveloped countries by increasing carbon dioxide and population density than in EU15 developed countries [71,72]. On the other hand, the findings show that the positive influence of economic growth on land-use change is greater in the EU15 members than in the EU13 members. In particular, the impact magnitude for EU15 and EU13 countries is 0.050 and 0.011, respectively. This indicates that a significant transformation in the natural landscape can occur in EU15 developed countries by increasing economic activates than in EU13 underdeveloped countries [73,74]. The findings point out that the negative impact of institutional quality on land-use change is greater in the EU13 members than in the EU15 members. Specifically, the influence magnitude for EU13 and EU15 countries is −0.050 and −1.032, respectively. This indicates that a significant improvement in the land-use change can occur in EU13 underdeveloped countries by increasing EU governance process for monitoring than in EU15 developed members.

3.3. Conclusions

The effects of sustainable hydropower on land-use change in the EU28 region were examined in the current study for the 1990–2018 period, using the FMOLS estimator, while the DOLS and Pooled OLS estimators were applied for robustness examinations. The findings show that land-use change may greatly increase as hydropower energy production increase in the EU28 region, especially in EU13 underdeveloped countries. Economic growth, carbon dioxide emissions, and population density are the instruments in increasing land-use change. On the other hand, institutional quality is found to be decreasing land-use change. The increase in land-use change can be easily influenced in the EU13 underdeveloped countries (for example Estonia, Czech, Slovak, Slovenia, Poland, and Romania), than in other EU15 developed countries (for example Germany, The United Kingdom, France, Italy, and Spain). This outcome is a result of the greater demand for conventional fuels in these members that can however be substituted with hydropower energy. Hydropower energy can play an important role in EU28′s drive to lower releases and to grow green-energy consumption. However, this would have significant implications on land-use change.

Improving the efficiency, optimization, and sustainability of large hydropower plants, while taking economic, social, and environmental factors into consideration, can be emphasized by the authorities of the EU28 region. To mitigate land-use change, this study suggests further investment in the sustainability of hydropower development. It would help to achieve energy stability, prevent further deterioration of hydromorphological conditions, and decrease over-reliance on other contaminated power sources. More hydropower will replace traditional energy in the manufacturing of products on a large scale to reduce emissions since the consumption of fossil fuel energy is an important factor in promoting CO₂ emissions. The efficacy and sustainability of hydropower to meet energy stability and to reduce conventional energy dependence. This will lead to the diversification of the clean and sustainable energy policy of EU28. It is significant to consider that whereas attempts are made to mitigate pollution by raising the output of hydropower, the change in land use should not be compromised. Since hydropower plants are the source of hydroelectricity, which can cause land-use changes, deforestation, and forest degradation.

3.4. Implication

The authorities of these countries should also stress the importance of integrating hydropower sustainability policies with land-use change policies to minimize the collateral effects of the deployment of hydropower energy and sustainability since unsustainable land use has significant negative impacts on the environment and society, which are likely to worsen. Policymakers must leverage synergies and manage trade-offs to create more sustainable land-use systems and land-use management with hydropower energy to achieve the international commitments for climate, biodiversity, and sustainable development of forestry and agriculture.

EU hydroelectric dams have impacts that are much more severe and wide ranging than what has been portrayed by dam proponents. Social impacts are devastating for the people who happen to live in the area of a dam, including not only those in the flooded area but also those downstream and upstream of the dam who lose vital resources such as fish. Indigenous peoples and other traditional riverside residents are often the victims. Environmental impacts extend to the entire river basin, including changes from altered sediment and water flows as well as a loss of aquatic fauna and loss or disturbance of vast areas of forests, floodplains, and other ecosystems. Therefore, changes in land use in this watershed affect the livelihood of the community and also affect the ability of the dam to deliver the planned economic and environmental benefits.

Therefore, the requirement for employing particular strategies to reduce the influence of these hydropower productions, such as the designing of the preserved zone and the determination of domestic revised parameters for environmental-economic segregation noting the ecological and social situations generated from the domestic factors that rely on the natural environment (land, water, air, plants, and animals) to survive such as autochthonous peoples, riverine population, and traditional fisheries. Finally, the study shows that there are both potential benefits and drawbacks of expanding hydropower generation in the EU region. While the benefits include an increase in revenue from the generation and export of electricity, side effects include the potential increase in land-use change which would reduce the environmental revenue. The study suggests implementing ecosystem restoration interventions to curb and offset some of these emerging environmental pressures and side effects. The authors propose reallocating part of the revenues to local environmental preservation can avoid most of the negative impacts forecasted.