Changes In Ecological Footprint Research Articles

Constructing a predicting model for ecological footprint in G20 countries through artificial neural network

The purpose of this research is to build an estimated model for the ecological footprint (EF) in the G20 countries in the period of 1999–2018, the last two decades. These countries have faced extensive EF changes and developments. This aim, indices (The Global Footprint Network and World Bank) has been used. Artificial neural network (ANN) models have been used for data analysis and index fitting. The findings of this research showed that the EF has been increasing in China with an average of (16.37), France (14.58), Brazil (9.37), and the United States (3.66). Root mean square error value in the first model (0.140), in the second model (0.0275), in the third model (0.0275) in the fourth model, is equal to (0.0608) and in the fifth model it is equal (0.11484). Therefore, the accuracy of EF prediction in neural network models 2 and 4 is 97.5% and 97.5%, respectively. Also, we believe that in better management of the EF, the use the ANN can be efficient and effective in forecasting the G20 countries. This study can provide two important contributions to the managers of this country in examining the environmental degradation of the G20 countries. First, the use of ANN is one of the methods of artificial intelligence that has a very high and acceptable accuracy in selecting research variables. Second, we used ANN models to solve the problem of prediction accuracy and selection of effective variables.

Bio‐innovation for environmental sustainability: Asymmetric nexus between bioenergy technology budgets and ecological footprint

AbstractAs the world grapples with sustainable energy and environmental preservation challenges, budgeting for bio‐resilience emerges as a pivotal step toward environmental sustainability. Our investigation delves into the influence of bioenergy technology budgets on the ecological footprint (ECF) in the top 10 nations that invest in bioenergy research and development (USA, China, Brazil, Germany, Japan, Canada, Sweden, Finland, Denmark, and the Netherlands). Prior research depended on panel data methods to explore the bioenergy technology‐environment nexus, disregarding the specific traits of individual countries. Contrarily, the existing research applies the quantile‐on‐quantile tool to improve the precision of our analysis by delivering a holistic worldwide viewpoint and customized perceptions for every economy. The findings indicate that dedicating budgets to bioenergy technology improves environmental quality by reducing ECF across several quantiles within our sample nations. Moreover, the outcomes uncover unique patterns in these relationships across multiple countries. These results stress the significance of policymakers conducting exhaustive assessments and implementing productive tactics to address bioenergy technology funding and ECF changes.

Teaching sustainability to accounting students: reducing their ecological footprint (EF)

Purpose: To analyze the changes in ecological footprint (EF) composition of accounting students caused by the teaching of sustainability.
 Methodology: Qualitative and quantitative approach, descriptive techniques and analysis of the collective subject discourse (CSD). Introduction to the topic and guidance to the WWF Brazil website to calculate EP.
 Results: In the first round, EF was 1.96 global hectares. Relevant components: food, government and transport. After cluster analysis, CSD analysis of the three clusters generated was performed, with red meat consumption being the main cause of the result. Students were instructed to reflect on changes to recalculate EF with new habits, revealing a reduction in the second round.
 Contributions of the Study: Consisted of a robust methodology to EF analysis, including the combination of cluster analysis with CSD.
 Conclusion: Confirmation of the academic role in environmental awareness to achieve sustainable development. It illustrated the importance of education for sustainability by changing students' attitudes.

Assessing the sustainability of urbanization at the sub-national level: The Ecological Footprint and Biocapacity accounts of the Budapest Metropolitan Region, Hungary

• EF and BC calculations can help assess urbanization at the sub-national level • EF and BC calculations show unsustainability in the Budapest Metropolitan Region; • An inverted U-shaped relationship between urbanization and EF cannot be justified; • Policies should aim to reduce the growth of EF, and halt the decrease of BC; • Growing environmental efficiency and new consumption habits are required; The growing concentration of people and wealth often results in imbalances of resource consumption and carrying capacity, therefore, the sustainability assessment of urbanization can offer an important basis for global sustainable transition. This paper aims to provide an analysis of the environmental sustainability of urbanization in Hungary focusing on the long-term changes of ecological footprint and biocapacity at the sub-national level, with a special attention to the Budapest Metropolitan Region (BMR). During the research a hybrid method considering an input-output model and household consumption data was used for the calculation of regional ecological footprint, whereas biocapacity was measured on the basis of land use data. Findings suggest, that even though the ecological deficit of the country has been gradually decreasing since the early 2000s, due to a shrinking population and increasing biocapacity, the ecological overshoot is still significant in the case of Budapest and its agglomeration (30 fold and 2.4 fold respectively). The unsustainability of the BMR is caused partly by demographic factors (7.6% population growth as opposed to the the 7.7% decrease in the countryside) and partly by rising per capita ecological footprint values (especially in the agglomeration from 2.73 to 2.92 gha/capita), which are not balanced by biocapacity on the supply side. This research concluded that policy makers in ageing societies with a highly centralized urban system like Hungary should launch programmes targeted specifically to primary metropolitan areas to improve environmental efficiency and encourage people to change their consumption behaviour.

Spatial and temporal changes of ecological footprint of water resources in Hohhot-Baotou-Ordos-Yulin urban agglomeration and evaluation of sustainable utilization

The Yellow River Basin is currently facing a fragile water ecological environment and severe water resources security situation, located in the Yellow River a few bends inside the city group of Hoh-hot-Baotou-Ordos-Yulin also presents the contradiction of limited water resources carrying capacity and in-creasing water demand. Based on the ecological foot-print model, the temporal and spatial variation characteristics of the ecological footprint of water resources in Hohhot-Baotou-Ordos-Yulin urban agglomeration from 2001 to 2019 are analyzed. The influencing factors of the ecological footprint are analyzed by using the correlation test, and the sustainable utilization of water re-sources in Hohhot-Baotou-Ordos-Yulin urban agglomeration is discussed. It is found that there is an obvious gap between the ecological footprint of water resources per capita and the ecological carrying capacity of Hoh-hot-Baotou-Ordos-Yulin urban agglomeration. The eco-logical pressure index is always higher than the standard value, and the ecological deficit of Ordos is the most serious. The contradiction between the social development of urban agglomeration and the utilization of water resources is more prominent, and the utilization situation of water resources is not optimistic.

Does Innovation Efficiency Suppress the Ecological Footprint? Empirical Evidence from 280 Chinese Cities.

Innovation is an important motivating force for regional sustainable development. This study measures the innovation efficiency of 280 cities in China from 2014–2018 using the super-efficiency slack-based measure and it also analyzes its impact on the ecological footprint using the generalized spatial two-stage least squares (GS2SLS) method and uses the threshold regression model to explore the threshold effect of innovation efficiency on the ecological footprint at different economic development levels. We find the corresponding transmission mechanism by using a mediating effect model. The major findings are as follows. First, we find an inverse U-shaped relationship between innovation efficiency and the ecological footprint for cities across China as well as in the eastern and central regions. That is, innovation efficiency promotes then suppresses the ecological footprint. Conversely, in western and northeastern China, improvements in innovation efficiency still raise the ecological footprint. Second, for the entire country, as economic development increases from below one threshold value (4.4928) to above another (4.8245), the elasticity coefficient of innovation efficiency to the ecological footprint changes from −0.0067 to −0.0313. This indicates that the ability of innovation efficiency improvements to reduce the ecological footprint is gradually enhanced with increased economic development. Finally, the industrial structure, the energy structure, and energy efficiency mediate the impacts of innovation efficiency on the ecological footprint.

Measuring the impacts of suburbanization with ecological footprint calculations

In this paper we present a complex Ecological Footprint (EF) analysis of one of the largest metropolitan regions in post-socialist East Central Europe, the Budapest Metropolitan Region. Our overall goal is to use both top-down and bottom-up approaches and measure the changes of footprint at a metropolitan scale between 2003 and 2013. Our specific objective is to explore how the spatial rearrangements of wealth, density and consumption influence the spatiotemporal changes of EF. The top-down (compound) calculations indicate growing footprint values both in Hungary and in the Budapest Metropolitan Region in the investigated period. However, household-level hybrid (component-based) calculations revealed decreasing footprint values for Hungary both in absolute and relative terms, and a growth for the metropolitan region. This finding suggests growing income disparities within the country. The indirect (consumption embedded) components of EF findings show that in the core city footprint values are higher due to higher disposable income. However, there is a gradual catching up in the suburban zone as younger and more affluent households arrive. On the other hand, direct per capita footprint values decreased in Budapest and grew in the suburbs between 2003 and 2013, mainly due to a higher heating footprint.

Dynamic changes of the ecological footprint in the Beijing-Tianjin-Hebei region from 1996 to 2020

Abstract The ecological footprint (EF) describes the complex relationship between the eco-environment and economic development. EF dynamics can better reflect the appropriation of natural resources in various countries or regions compared with previous studies. This paper identified six dominant factors including population, per capita GDP, three major industrial added values, and energy consumption using grey correlation model for the EF changes of the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region from 1996 to 2015. Then, we predicted the EF of the Jing-Jin-Ji region from 2016 to 2020 based on support vector machine model. (1) Since 1996, the EF and per capita EF of the overall Jing-Jin-Ji region have increased, of which those of Hebei Province and Tianjin increased while Beijing decreased. (2) Energy consumption dominated the EF of Jing-Jin-Ji region. The population of Beijing had a high correlation coefficient of 0.735 with the local EF, and the degree of correlation between the EF and per capita GDP was 0.812 in Hebei Province. In Tianjin, the added value of the tertiary industry was closely correlated with its EF, with a correlation coefficient of 0.741. (3) The EF of the Jing-Jin-Ji region will reach 778.30 million hectares by 2020, an increase of 10% compared with the value in 2015. Finally, suggestions for the development of industrial structure and energy consumption are listed for the sustainable development of Jing-Jin-Ji region.

Energy Ecological Footprint Changes and Its Influencing Factors at the Provincial Scale in China

The energy ecological footprint is an important theory and method for quantitative measurement of the coordinated development of the socio-economic-ecological environment. This research indicated the following: (1) the energy ecological footprints of China and the provinces showed significant linear increases (except Beijing). China’s economy developed rapidly over 1999-2015, but the economic development had a high dependence on energy consumption, which induced an increase in the energy ecological footprint. Optimizing industrial structure and reducing energy consumption are effective ways to reduce the energy ecological footprint. (2) Forest land was the most important land use type for per capita energy ecological footprint capacity, accounting for 50% or more of the total. The energy ecological footprints of China and the provinces were mainly in a state of deficit, and only Sichuan, Qinghai, Guangxi, Shaanxi, Inner Mongolia and Xinjiang were in surplus. In addition, the ecological pressure in the eastern developed provinces was higher than in the central provinces, and the lowest pressure was in the west. (3) The energy ecological footprint of China and the provinces was mainly affected by the secondary industry output value, followed by population, total investment in fixed assets, and then the tertiary and primary industry output values. The results showed that reduction of industrial energy consumption is still the main issue for addressing carbon emissions in China.

An accurate ecological footprint analysis and prediction for Beijing based on SVM model

Accurate prediction of the ecological footprint (EF), an effective indicator for measuring urban sustainable development, enables better protection of urban ecosystems and alleviates discrepancies in urban development, resource utilization, and environmental protection. In contrast to previous research using general methods, we introduce the support vector machine (SVM) method. A novel model with improved prediction accuracy based on SVM is proposed, and we deploy this method to predict the EF of Beijing between 2016 and 2020. First, we calculate the EF of Beijing between 1996 and 2015 and screen out the 6 dominant indicators of EF changes using partial least squares (PLS). Second, based on 2014 and 2015 EF data, we compare the prediction accuracy of the back propagation neural network (BPNN) with the SVM using the 6 indicators as inputs and EF as the output, which then allows us to predict the year 2020 EF in Beijing. The results demonstrate that (1) the relative error rates between the prediction value and the actual value using the two models are 2% and 1% in 2014 and 3% and 0.53% in 2015, respectively, and the fact that the standard deviation of the SVM approaches zero demonstrates its higher prediction accuracy and stability compared to the BPNN; and (2) the EF of Beijing almost doubled to 8984 ten thousand acres from 1996 to 2015 and is predicted to increase to up to 14,206 ten thousand acres by 2020. Based on our prediction model, we provide science-based suggestions for the future development of Beijing.

Evaluating the Sustainability of Nature Reserves Using an Ecological Footprint Method: A Case Study in China

Nature reserves are established to protect ecosystems and rare flora and fauna. However, with the rapid development of the social economy, many nature reserves are facing enormous pressures from human activities. The assessment of the sustainability of nature reserves is a fundamental task for the planning and management of such areas. In this study, the sustainability of China’s 319 national nature reserves (NRRs) was evaluated based on an ecological footprint (EF) method. The results indicated that the per capita ecological footprints of all national nature reserves increased 85.86% from 2000 to 2010. Meanwhile, the per capita biocapacity (BC) of all national nature reserves increased slightly, with a rate of increase of 1.79%. The ‘traffic light’ method was adopted to identify the sustainability status of those national nature reserves. It was found that currently (2010) 45% of NRRs were in the condition of ecological deficit. In terms of dynamic changes in EF and BC, only 16% of NRRs were sustainable. The 124 national nature reserves that were in the red light state were mainly distributed in Anhui Province, Chongqing City, Hunan, Guizhou, Fujian, Shandong Province, and Inner Mongolia. The percentage of nature reserves at the red light state in these areas were 83.3%, 66.7%, 64.7%, 62.5%, 58.3%, 57.1%, and 56.5%, respectively. The reserves in the red light state should be included in the priority concern level and should be strictly controlled in terms of population growth and the intensity of exploitation. The results of this study will provide more effective data for reference and for decision making support in nature reserve protection.

The construction and application of a carrying capacity evaluation model in a national park

This work applies ecological footprint analysis to establish the framework of a carrying capacity model for a national park. The model estimates changes in ecological footprint and biocapacity indicators for Taroko National Park (Taiwan) from 2002 to 2011. The findings indicate that: (1) In the past 10 years, the biocapacity status of Taroko National Park has remained within the environmental capacity; however, the per-capita ecological reserve decreased by 73.38 % between 2002 and 2011, from 0.085010 global hectares to 0.022633 global hectares. Evaluation of the ecological footprint intensity index reveals that the park has maintained a level 1 status, the highest ecological security level; (2) Linear estimation undertaken in the study predicts that when the ecological footprint intensity index reaches 0.5—when the ecological security level falls from level 1 (good) to level 2 (fair)—the critical tourist-carrying capacity would be 3,380,351 visitors per year, or 1.18 times the number of tourists in 2011. Furthermore, when the ecological footprint intensity index reaches 0.8—when the ecological security falls to level 3 (poor)—the critical tourist-carrying capacity would be 5,408,561 visitors per year, or 1.88 times the number of tourists in 2011. This work adopted the ecological footprint method and the threshold ecological footprint intensity index value to estimate the critical number of tourists for the sustainable biocapacity of Taroko National Park. The results are intended to serve as a reference for national park administrators to develop policies on tourist carrying capacity.

Dynamic Analysis and Prediction of Ecological Footprint in Jilin Province of China Based on Grey Prediction Model

The paper calculates ecological footprint per capita and ecological capacity per capita in the Jilin province during 1998 and 2010 by using the ecological footprint theory, and analyzes the dynamic changes of ecological footprint per capita and ecological capacity per capita, and obtains development prediction model of ecological footprint per capita and ecological capacity per capita based on grey prediction model. The results indicate the ecological footprint per capita had increased continuously from 1.7841 hm2 per capita to 3.2013 hm2 per capita between 1998 and 2010. During this period, ecological capacity per capita dropped from 1.3535 hm2 per capita to 1.3028 hm2 per capita. Ecological deficit had increased from 0.4306 hm2 per capita to 1.8985 hm2 per capita that showed that the development of Jilin province was in an unsustainable status. The gray prediction model shows the ecological footprint per capita in the Jilin province will increase from 3.4833 hm2 per capita to 5.7022 hm2 per capita between 2011 and 2020, ecological capacity per capita will drop from 1.2978 hm2 per capita to 1.2676 hm2 per capita and ecological deficit will increase from 2.1855 hm2 per capita to 4.4346 hm2 per capita.

梁子湖区生态足迹与生态承载力空间动态变化分析

利用生态足迹理论，计算分析了梁子湖区2007~2011年5年间生态足迹变化，结果表明，梁子湖区人均生态足迹由2007年的1.73517 hm2上升到2011年的3.03992 hm2，人均生态承载力由2007年的0.54175 hm2上升到2011年的0.62625 hm2，由于对资源的需求量大于供给量，所以梁子湖区生态赤字也在逐年提高。梁子湖区处于不可持续发展的状态，特别是对于水资源的生态赤字持续增长。梁子湖区万元GDP有上升的趋势，说明梁子湖区资源利用率有所下降。同时对梁子湖内部生态足迹做了空间分析，也提出了减少区域生态赤字的措施。 The analysis on the dynamic changes of ecological footprint and ecological capacity in Liangzi Lake District showed that in 2007-2011, the ecological footprint per capita in the district increased form 1.735171763 hm2 to 3.039921491 hm2, while the ecological capacity per capital increased form 0.541747253 hm2 to 0.626252988 hm2. Be- cause the demand for resources is greater than the supply, ecological deficit increased year by year. Liangzi Lake Dis- trict is in a state of unsustainable development, especially for the sustainable growth of the ecological deficit of water resources. As a whole, the ecological footprint per ten thousand yuan GDP increased in the five years, indicating that the resources efficiency in the district was descended gradually. Based on the present situation, we made an internal space analysis and put forward some measures for reducing the ecological deficit.

Decomposition Analysis of Energy Ecological Footprint Change in Bohai Rim Region

Using ecological footprint and decomposition analysis models, energy ecological footprint (EEF) from 2000 to 2008 in Bohai Rim Region was calculated. The EEF change and the impact factors were also analysed. The results showed that the EEF increased continuously from 2000 to 2008. The total values of EEF increased to 4.578×108 hm2 in 2008 from 2.159×108 hm2 in 2000, and the values per capita increased to 1.946 hm2 from 0.974 hm2. These contributed 50-60 percent of regional total ecological pressure. In all contribution factors, GDP growth was highest, energy structure and population size were inferior, and energy strength was lowest. Though the energy strength was decreased continuously, this was not enough to counteract the ecological impact derived from immoderate energy structure and consumption level. This led to energy ecological footprint in Bohai Rim Region increasing continuously.

Analysis of the major drivers of the ecological footprint using the STIRPAT model and the PLS method—A case study in Henan Province, China

Taking Henan Province of China as an example, we computed and analyzed the ecological footprint (EF) in 1983–2006. The results showed that the EF in Henan Province quadrupled in the 23 years, but its ecological carrying capacity (EC) was rather low and was in a state of slow decline, indicating that Henan's ecological deficit (ED) had become a remarkable social problem. Therefore, the major drivers of the EF's change were analyzed. According to the simulations with STIRPAT model, the major drivers of Henan's EF were human population ( P), GDP per capita ( A 1), quadratic term of GDP per capita ( A 2), percent of economy excluded in the service sector (Ta 1) and percent of urban population (Tb 1). However, these drivers themselves had strong collinearity, which might produce some uncertain impact to the final results. In order to avoid the impact of collinearity, the method of partial least squares (PLS) was used. The results showed that the major drivers of EF were P, A 1, A 2 and Tb 1. Ta 1 was excluded. Compared with the results by the STIRPAT model, which showed that P is the most dominant driver and the effect of the other drivers could almost be ignored, the results by PLS method were considered as more reasonable and acceptable because the impacts of the A (Affluence) and T (Technology) conditions to the regional EF were still too important to be ignored. In addition, the results acquired by both methods showed that the curvilinear relationship between economic development and ecological impact (EF) or the classical EKC hypothesis didn't exist in Henan Province.

Change of ecological footprint and analysis of ecological sustainability—Taking Zhangjiakou City as an example

This paper researches the ecological sustainability of Zhangjiakou City, Hebei Province, China, using the ecological footprint model. According to the study we find that Zhangjiakou City was in the situation of ecological deficit from 1990 to 2000 and the deficit had the enlarging tendency. In 1990 the per capita ecological footprint was 0.964 and the per capita ecological capacity was 0.518, thus it can be calculated that the per capita ecological deficit was −0.446. However in 2000, the per capita ecological footprint increased to 1.068, at the same time the per capita ecological capacity decreased to 0.471, then the per capita ecological deficit in 2000 was −0.597. Furthermore, this paper studies the ecological sustainability of the city from the changes of the ecological footprint of per 10,000 yuan GDP and the productivity of ecological system. Finally the authors point out the shortage of the model and the way to improve it.