Study on the Impact of Population Aging on Regional Economic Growth: A Case Study of Beijing-Tianjin-Hebei

The impact of population aging on regional economic growth is an important issue to improve the quality of economic development and avoid economic risks. A case study of BeijingTianjin-Hebei region shows that there is a large proportion of the elderly population in Beijing-TianjinHebei region and a large regional disparity in population aging. Through the establishment of a double-panel variable coefficient fixed effect model and panel co-integration test, it is found that there is a long-term and stable equilibrium relationship between population aging and regional economic growth. Whether or not there is a time effect, the aging of the population has a positive effect on the economic growth of Beijing and Tianjin, and has a negative impact on the economic growth of Hebei Province.

Introduction
Population aging is a major realistic problem facing mankind in the 21st century. It is related to the overall situation of economic and social development and involves many aspects of social economy, family life and personal life cycle. China has entered an aging population society in 2000.The problem of population aging has begun to emerge and will have a significant impact on China's on economic and social development. Beijing-Tianjin-Hebei region includes two municipalities directly under the Central Government of Beijing and Tianjin, as well as 11 prefecturelevel cities of Shijiazhuang, Baoding, Langfang, Tangshan, Chengde, Cangzhou, Hengshui, Zhangjiakou, Qinhuangdao, Xingtai and Handan in Hebei Province. The level of population aging in Beijing, Tianjin and Hebei is higher than that in most provinces of China, and the problem of population aging is becoming increasingly prominent. Based on the example of Beijing-Tianjin-Hebei, the study of how population aging affects regional economic growth has become a "proactive" strategy to improve the quality and level of economic development in Beijing-Tianjin-Hebei and to avoid long-term economic development risks.

Summary of Research
The impact of population aging on economic growth is extensive and far-reaching.From the effect of population aging on economic growth, scholars at home and abroad hold different views,which can be attributed to the positive,negative and positive effects of population aging on economic growth.
On the one hand,the increasing effect of population aging on per capita output is greater than the decreasing effect of per capita output caused by the decrease of birth population,indicating that over time,aging will promote economic growth [1].The increase of government and residents'health expenditure caused by aging will play a significant positive role in economic growth by increasing the total consumption [2]. Aging will help to increase human capital investment,improve the quality of labor force,and promote the formation and development of the elderly market [3].On the other hand,China has the existing situation of "getting old before getting rich",which will hinder its economic development [4]. Aging society will hinder economic growth through the decline of working age population and savings rate [5].Aging will lead to the decline of labor participation rate in the future and have a negative impact on economic growth [6].
In addition, based on the extended Solow model,we find that aging has a dual effect on China's economy,and the two effects are different in magnitude.Aging will increase its negative impact on the economy [7].Some scholars believe that aging will have a moderate negative impact on the macroeconomy,and at the same time,it will also have a positive impact [8].
To sum up,the research on population aging covers all aspects of economic growth and is more comprehensive.Generally speaking,there are few empirical studies on the impact of population aging and economic growth in the existing literature,and relatively few studies on the medium-level of a certain region.At the same time,the establishment of economic growth model is relatively single.Therefore,this paper summarizes the shortcomings of existing literature,studies the impact of population aging on regional economy in Beijing-Tianjin-Hebei region, chooses the main indicators of population aging and economic growth,considers the impact of time effect on economic growth,and tries to measure the impact of population aging on economic growth scientifically and accurately,so as to promote the coordinated and high-quality development of Beijing-Tianjin-Hebei economy.

The Impact of Population Aging on Regional Economic Growth in Beijing-Tianjin-Hebei
Based on the population aging and economic data from 2002 to 2016,this paper analyzed the impact of population aging on regional economic growth in Beijing-Tianjin-Hebei.

Data Selection and Model Construction
In order to reflect the level of population aging more truly and comprehensively,the proportion of the elderly population in Beijing-Tianjin-Hebei is selected to measure the level of population aging,and GDP per capita is selected as the index to measure the economic growth of Beijing,Tianjin and Hebei.On this basis,fixed assets investment is added as the control variable.In order to make the model more scientific and effective to measure the impact of population aging on economic growth, two models including time effect and non-time effect were established to explore.The econometric models are as follows:

Testing of Model
Because there may be false regression in time series,unit root test is needed for each variable before panel data analysis,and unit root test for model 1 will be carried out.In order to improve the reliability of the test, LLC test and Fisher test will be used to verify the stationarity of the data.The unit root test results of model panel data are shown in Table 1. Note: *** denies the original assumption of unit root at 1% significance level.
The results of Table 1 show that the original assumption with unit root is rejected after the firstorder difference of each variable is 1% significant level, that is, the variables LNPEP, LNPGDP and LNIFA are stationary series, which shows that pseudo-regression can be effectively avoided in the following panel data model. The first-order difference terms of variables in the model are stationary series,so the cointegration test can be carried out. Table 2 shows the results of panel co-integration Pedroni test.There are differences in the test results.Because group ADF, panel ADF and group PP test are effective, and the three statistics reject the original hypothesis that there is no co-integration relationship at the level of 1% significance,it can be concluded that there is co-integration relationship among variables, namely, per capita GDP, the proportion of the elderly population and fixed assets investment.The long-term equilibrium relationship can be estimated by panel model. .795192 *** Note: * * and *** respectively deny the original hypothesis without co-integration at the level of 10% and 1% significance.
Before constructing the panel equation, it is necessary to identify whether the mixed cross-section model, the variable intercept model or the variable coefficient model are selected, and to verify whether the model set above is reasonable. The research decides which model to choose by constructing F statistic, which is tested by constructing F1 Statistic and F2 statistic. The calculated values of F1 Statistic and F2 statistic are1070.6 and 42.5 respectively, which are larger than their corresponding critical values. Therefore, the mixed cross section model and the variable intercept model are rejected. The variable coefficient model is adopted, and the time effect is taken into account in model 2 on the basis of model 1.The time dummy variable is added, and the fixed effect model with variable coefficients is still adopted.

Panel Empirical Results and Explanations
The estimated results of panel data are shown in Table 3.The goodness of fit of model 1 and model 2 is higher, and model 2 has better goodness of fit , and the F statistics of the two models are 926.1629 and 480.1394,respectively,which have passed the 1% significance level test, and the model as a whole is remarkable. From model 1, population aging has a positive effect on the economic growth of Beijing and Tianjin, and the positive impact of population aging on Beijing is greater than that of Tianjin. The proportion of the elderly population increases by 0.01, its contribution to Beijing's economic growth is 0.25, and its contribution to Tianjin's economic growth is 0.15. The corresponding regression coefficient test is not significant. The population aging in Hebei has a negative effect on economic growth, reflecting the elasticity of -0.33. The regression coefficient is not statistically significant, indicating that the population aging has a negative impact on the economy of Hebei.
The empirical results show that the current population aging has a positive impact on Beijing and Tianjin. In the short term, the aging population will not have a negative impact on the supply of Beijing-Tianjin labor force. Beijing and Tianjin have always been important places for population migration in China.Although the registered population of Beijing has declined in recent years, the huge siphon effect produced by the abundant public resources such as education and medical treatment has caused a large number of young people to enter Beijing and Tianjin to work and live.The deepening of the reform of the household registration system has also increased the high-quality human resources in Beijing and Tianjin to meet the needs of enterprises for labor, which keeps the vitality of human capital needed by the city. At the same time, the quality of the elderly population in Beijing and Tianjin is generally high. Although the elderly have reached retirement age, their get mature experience and excellent technology may still be employed by the government or enterprises in the form of expert consultants, so as to give full play to the "waste heat" of the elderly with high knowledge and skills,carry out their old achievements,meet the needs of the society for high-end talents,and make them continue to serve the economic and social development.Aging will promote Beijing-Tianjin economy to "change its mode, adjust its structure", promote capital economic development, and contribute to social progress and cultural prosperity.
Population aging has a negative impact on Hebei Province. Due to the lack of abundant resources such as education and medical treatment, a large number of young laborers in Hebei Province are pouring into big cities such as Beijing and Tianjin, which makes the brain drain in Hebei Province, coupled with the aging of population structure, easily leads to structural unemployment,causes the problem of "difficult recruitment" in Hebei enterprises and hinders economic growth;Hebei has a large elderly population,and the rural aging is serious.According to the life cycle theory,the elderly are from When people quit their jobs,their incomes are low or even no income, which leads to a decrease in savings and consumption, which is not conducive to expanding domestic demand and affecting the improvement of people's income level. The physical fitness of the elderly and their ability to receive new knowledge and skills are lower than those of the young people.Aging has reduced the labor participation rate and per capita output in Hebei Province. The empirical results also show that the degree of population aging in Hebei is not in harmony with economic development, and economic growth lags behind the development of population aging. Model 2 adds time effect to model 1. The results show that the aging population in Beijing and Tianjin still has a positive effect on economic growth, and the contribution of aging to economic growth is improved on the basis of model 1. The population aging in Hebei Province still has a negative impact on economic growth,but the negative effect has been reduced.The regression coefficient is -0.3, which is not statistically significant.The aging population has promoted the economic growth of Beijing and Tianjin through labor force,savings,consumption and so on, and the driving force is more obvious.Since 2002,the elderly population in Beijing, Tianjin and Hebei has increased year by year.The high price of house prices and living costs in Beijing and Tianjin are significantly higher than those in Hebei, which makes many elderly people transfer to Chengde,Qinhuangdao and other cities with better environment and lower cost to provide for the aged,and promotes the development of the local pension industry. To some extent, it offsets the negative impact of population aging on the economy. The time dummy variable shows different contribution to the per capita GDP of Beijing, Tianjin and Hebei provinces in the model. Before 2006, fictitious variables had weak negative effects on Beijing and Hebei. After 2006,the negative effects of fictitious variables on Beijing were further mitigated, and they had positive effects on Tianjin and Hebei.Controlled variable investment in fixed assets shows a significant positive impact on the per capita GDP of Beijing, Tianjin and Hebei in both models.Increasing investment in fixed assets, such as infrastructure, is bound to help stimulate economic growth.

Conclusion
The present situation of population aging in Beijing,Tianjin and Hebei is described and analyzed.It is found that there are correlations and heterogeneities in the aging process in Beijing-Tianjin-Hebei.The proportion of the elderly population in Beijing-Tianjin-Hebei is higher than that in most parts of the country.The proportion of the elderly population is significant,and there are big differences among the aging cities.By building a fixed effect model with double panel variable coefficients and panel co-integration test,it is found that there is a long-term stable equilibrium relationship between population aging and regional economic growth in Beijing-Tianjin-Hebei.Wheth-er or not there is a time effect,Population aging has a positive impact on economic growth in Beijing and Tianjin, and has a negative impact on economic growth in Hebei Province.


s2fieldsofstudy: Economics Business

text:
ANALYSIS OF ISLAMIC BANK INFLUENCE ON AGRICULTURAL FINANCING SECTOR PERIOD 2014-2016

One main problem that Indonesia’s agricultural sector faces is limited financial acces. This research tries to analyze Islamic Banking influence on agricultural financing sector in Indonesia. It utilizes monthly data of Islamic banking statistic from January 2014 until December 2016. This study conducts OLS (Ordinary Least Squares) as its analytical method to intrepret the data analysis. The finding of this paper shows that the incentive of Islamic Bank Indonesia Certificate (SBIS), third party fund, inflation, Non Performing Financing influence agricultural financing significantly. But, interest rate is not affecting agricultural financing. This research suggests the increase in agricultural financing proportion for agriculture should be in accordance with increase in total deposit value. This finding recommends improvement of the human resources competency in agriculture sector as well as strengthening instruments of Islamic financing policy.

INTRODUCTION
As one of the sectors that play an important role in the national economy, the agricultural sector still faces several obstacles including the lack of access to financing sources, therefore, there is a need for a financing model that is able to provide stimulus to agricultural businesses to increase their production, the majority of farmers in Indonesia who only have small-scale businesses, namely the agricultural sector in general still rely on their own capital in the development of their business (Beik 2013).
As an agricultural country, the agricultural sector has a very strategic role in national development. The importance of the agricultural sector among other sectors is as a source of livelihood for the majority of the population in Indonesia. It has also contribution to GDP, the exports, industrial raw materials and the provision of food and nutrition. The agricultural sector also proved to be able to support the national economy in the event of an economic crisis (Ashari 2005). Although very strategic, the agricultural sector is often faced with many problems, especially weak capital as an essential element in increasing the production and living tariffs of rural communities, the lack of capital can limit the movement of this sector.
In addition, the problem of bankability and perceptions of banks that consider agriculture as a high risk industry are the two factors that cause low rates of lending and bank financing in the agricultural sector. While the weak access of small farmers to banking financial institutions is caused by non-simple procedures and requirements that must be met by farmers.
On the other hand, the banks themselves are less interested in financing the agricultural sector which is considered high risk, both due to natural disturbances such as floods, droughts, pest and plant diseases, and price fluctuations (Beik 2013). For this reason, it is necessary to develop appropriate and responsive strategies in managing the development of the agricultural sector so that Indonesia has a strong bargaining position. Agricultural development is directed to increase farmers' income through increased farming productivity and value-added products, as well as agricultural product distribution. This aspect requires funding in the form of financial support in the form of working capital.
Agricultural financing in conventional banks is considered to be less effective because of the interest set by the government.
This makes farmers as additional returns, the financing gap between the borrower and the funder where the two parties are not in full synergy, each moves itself in a different calculation because the creditor is using more monetary sector analysis while the debtor in the real sector activities. The banking sector has not provided optimal support in increasing the amount of lending and the ease of obtaining capital loans to the agricultural sector, especially small farmers. Therefore an alternative source of financing is needed for the agricultural sector. One alternative that can be developed is financing through Islamic banking. With the characteristics of Islamic banking based on the real sector, the pattern of Islamic financing for the agricultural sector is expected to be well developed (Nasution 2006).
In the world of banking itself consists of two forms, namely conventional and Islamic banks. The conventional bank in its activities uses an interest system that is inspired by the capitalist economic system by attracting business profits mainly from credit interest which is utilized through public savings funds which are then borrowed back with additional interest, while the Islamic banking principles are based on Islamic law and does not recognize interest but profit sharing.

Definition of Islamic Banking
The Bank is a financial institution, which is an entity that functions as a financial intermediary or financial intermediary of two parties, namely the party that is over-funded and the party that is under-funded. As an important institution in the community, the bank is a financial institution whose main business provides credit and services in payment traffic and money circulation (Sinungan 1993). Bank is simply defined as financial institution whose business activities are collecting funds from the community and channeling the funds back to the community and providing other bank services (Kasmir 2010).
Islamic banks are banks that are in their activities, both fund raising and in the context of channeling funds to provide and impose compensation on the basis of sharia principles, namely buying and selling and profit sharing. Islamic banks are referred to as banks without interest because in raising funds do not provide interest payments, and in loans are not charged interest.
Islamic banks are referred to as banks without interest because in raising funds do not provide interest payments, and in loans are not charged interest (Herman 2006).
According to Muhammad (2005) Islamic banks are banks that operate without relying on interest. Islamic banks or commonly referred to as interest-free banks are financial / banking institutions whose operations and products are developed based on the Al-Quran and Sunnah. In other words, Islamic banks are financial institutions whose main business provides financing and other services in payment traffic as well as circulation of money whose operations are adjusted to Islamic law. Islamic banks are banks that carry out their business activities based on Islamic principles, namely the rules of interbank agreements with other parties (customers) based on Islamic law. So the difference between the Islamic banks with a conventional bank is located on the basic principle of operation that does not use interest, but use the principle of sharing, buying and selling and other principles in accordance with the Islamic Shari'a, because the flower is believed to contain elements of usury is forbidden (prohibited) by Islam. (Rivai 2007).

sector in Indonesia
At present the national bank's alignment with the agricultural sector is very low. Based on BI data, national bank lending as of Therefore, the role of Islamic banking is expected to drive the agricultural sector in Indonesia.
Islamic banks are more appropriate to play a role in agriculture than conventional banks. This is based on several things. First, because philosophically, Islamic banking has strong ties with the agricultural sector. Farmers who have been accustomed to profit sharing systems such as maro, gaduhan and others make it easier for Islamic banks to enter the heart of the agricultural sector. Second,

Role of Agriculture Industry
Agricultural Agriculture Sector is an industry that organizes production factors (land, minerals, capital, management, labor) to produce and market food and fiber (Purwaningsih 2017) . For a country that will carry out its economic development, there are a number of choices that will certainly be adopted. There is a country that in its economic development relies on the industrial sector and then follows other sectors. There are countries that prioritize the agricultural sector than others. And there are also countries that choose a combined alternative, namely balancing between the industrial sector and the agricultural sector. In

addition, in reality agriculture is indeed an important sector in the
Indonesian economy because up to now around 63 percent of the population is foraging in the agricultural sector (Prayitno 1985).
The agricultural sector can be classified into several sub-sectors, namely the food crop sub-sector, the plantation sub-sector, the livestock sub-sector and the fisheries subsector.

Certificate of Islamic Indonesia Bank (SBIS)
Bank Indonesia legislation Number 10/11 / PBI dated March 31, 2008 concerning Bank Indonesia Sharia Certificates. Bank Indonesia Sharia Certificates (SBIS) are securities based on short-term Sharia Principles in rupiah currency issued by Bank Indonesia.

Third Party Funds (TPF)
The bank's efforts in raising funds dominantly affect the development of the bank. In finding sources of bank funds, banks must consider factors such as the level of ease in obtaining the source of funds or the costs incurred by the bank to obtain it.
Sources of funds from the community are the main source of funds for banks. This source of funds is easy to find and is also available in large numbers in the community and the conditions are not too difficult.Banks only need to attract the public by providing promos or providing products that are easy to terms and conditions.
Based on Bank Indonesia Circular No. 6/23 / DPNP dated May 31, 2004 the funds entrusted by the community to the bank can be in the form of demand deposits, savings deposits. After fundraising, bank wouldi redistribute these funds to the public or better known as credit (Kasmir 2001).Credit is the bank's most important activity in generating profits (Dendawijaya 2003).
Like financing theory Karim (2004) stated that one source of funds that can be used for financing is own capital, so that the greater the available funding sources, the bank will be able to channel financing in the larger maximum limit, especially in the agricultural sector.

Inflation and Islamic Perspective
The inflation definition is as varied as we can find in the economic literature. This diversity of definitions occurs because of the extent of the influence of inflation on various sectors of the economy. The close and broad relationship between inflation and various sectors of the economy gives rise to our differences in understanding and perception of inflation, as well as in formulating policies for the solution. However, in principle there are still some unity of view that inflation is a phenomenon and economic dilemma. Inflation is a condition that indicates the weakening of purchasing power which is followed by the decreasing real value of a country's currency.
According to Islamic economists, inflation is very bad for the economy because of the following four thing. First, it causes disruption to the function of money, especially to the function of savings (saving value), the function of upfront payments, and the function of unit calculations. Second, as a result of the inflation burden, people must break away from money and financial assets.
Third, there are weakening the spirit of the community to save (the decrease in marginal propensity to save) and increasing tendency to shop, especially for non-primary and luxury goods.
Fourth, direct investment in unproductive things such as the accumulation of wealth in the form of land, buildings, precious metals and foreign currencies as well as sacrificing productive investments such as agriculture, industry, trade and transportation (Karim 2010).

Relation of of Inflation Levels to Financing
Inflation is a general increase in the price of goods / commodities and services continuously in a certain period.
Inflation can cause disruption to the savings function (value of deposits). Islamic anks as one of the players in the banking finance industry are not spared from the effects of inflation.
The close and broad relationship between inflation and various economic sectors gave rise to our differences in understanding and perception of inflation, as well as in formulating policies for the solution. However, in principle there are still some unity of view that inflation is a phenomenon and economic dilemma.
Inflation is a condition that indicates the weakening of purchasing power which is followed by the declining real value of a country's currency (Khalwaty 2000).
Inflation can cause a high risk of default. This risk will increase non-performing financing of Islamic banking. So that when the inflation rate is high, the bank will be very careful in distributing financing. In addition, inflation can also put pressure on Islamic banks in terms of raising funds from the public, rising and falling inflation will affect the level of public saving, so that it will affect the financing of Islamic banks

Non Perfoming Loans
In the banking business, the most profit is from financing or providing credit to the public. Although financing is one of the sources of bank income, financing also has risks, namely the occurrence of non-performing financing such as substandard loans, doubtful financing and bad financing. Non Performing Financing is a credit that is classified as doubtful and loss. The terms doubt and loss here refer to the Bank Indonesia provisions adopted by Indonesian banks. Whereas according to Suhardjono Financing is a problematic financing which is categorized as loss.

Research Variables
The definition of the research variable is something in the form of what is determined by the researcher to be studied until information is obtained about it, then drawn conclusions. In this study.
Independent variables are variables that influence or cause changes or the occurrence of the dependent variables (Sugiyono 2004 (Sugiyono 2004). In this study the dependent variable is the financing of Islamic banks in the agricultural sector in Indonesia. Dajan (1996) stated that the population is the whole element which has one or more of the same characteristics or characteristics.

Population and Sample
The population used in this study is a report on Islamic banking statistics. The sample according to Sugiyono (2004) is part of the number and characteristics possessed by the population.
The sample taken in this research is a report on Islamic banking statistics for three years from 2014-2016. In this study the authors used convenience sampling, namely the sample members that selected based on the ease of obtaining data to measure

Data Collection Method
The data obtained in this study is secondary data derived from literature / other sources from inside and outside of Islamic banking, while data collection techniques are as follows Secondary data Secondary data is data obtained from other parties

Data Analysis Model
To examine the influence model and the relationship of the independent variables which are more than two variables to the dependent variable, multiple linear regression method is used (Ghozali, 2006

Multicollinearity Test
Test Multicollinearity test aims to test whether in the linear regression model there is a correlation between period t disturbing errors with confounding errors in the t-1 period (Ghozali 2006). Multicollinearity means that there is a strong linear relationship between independent variables with one another in the regression model. A good regression model is that there is no linear correlation / strong relationship between the independent variables. If there is a symptom of multicollinearity in the regression model, then the regression model cannot appraise precisely so that the wrong conclusions about the variables studied are obtained.

Autocorrelation Test This test aims to test whether in a linear regression model
there is a correlation between confounding errors in period t with confounding errors in the period (Ghozali 2006)   Whereas if the test of the degree of determination approaches 1 then it can be said that the stronger the model is in explaining the variation of the independent variables on the dependent variable (Suharyadi & Purwanto 2009)

The Profitability Development of Islamic Commercial Banks
This study utilized computer software Eviews 10 as data processing tools to speed up the processing of results that can explain the variables with econometric analysis methods. The results and analysis of the tests that have been carried out as the compared to α. If α is smaller than prob-chi-squared then it is free from the problem of heteroscedasticity. Based on table1 above shows that prob-chi-squared is 0.0795> 0.05, it indicates that it is free from the problem of heteroscedasticity.

Autocorrelation Test
Autocorrelation test aims to test whether in a linear regression model there is a correlation between confounding errors in period t with confounding errors in period t-1 (previously). 6. T test (partial) After carrying out the overall regression coefficient test, the next step is to calculate the regressioncoefficients individually or t test. The t test is used to determine whether or not the influence of each independent variable individually (partial) on the dependent variable is tested at a significant level of 0.05, the independent variable influences the dependent variable. The results of testing hypotheses with the t test are as follows:

Economic Interpretation
The interpretation of the results of this study is as follows: 1 This research is supported by previous research conducted by Dahlan (2015) which showed a coefficient with a negative and significant direction.


The Macrofungi in the Island of San Antonio, Northern Samar, Philippines

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/ by/4.0) ABSTRACT This study aimed primarily to identify the different macro fungi species in the Island of San Antonio, Northern Samar. Specifically, its objectives were: to identify the different species of macro fungi in the study are; to classify hierarchically the macro fungi species; to determine the kind of substrate where macro fungi grow; and to know the economic uses of macro fungi in the study area. It was conducted in the Island of San Antonio Northern Samar, which is composed of ten (10) barangays namely: Vinisitahan, Dalupirit, San Nicolas, Rizal, Manraya, Pilar, Burabod, Ward III, Ward II, and Ward I during the months of October and November 2012.

INTRODUCTION
Fungi were long classified as members of the plant kingdom because they appeared to be similar to plants. However, fungi differ from plants in several distinct ways. Compare what you have learned about cell wall composition in fungi and plants. The cell walls of plants are composed of cellulose, whereas the cell wall of almost all fungi contain chitin; in addition, plants are autotrophs, whereas fungi are heterotrophs. They cannot make their own food, and because fungi, and because of these and other differences, fungi are now classified in their own kingdom. However, because fungi were once classified plants, the main groups of the kingdom fungi are divisions rather than phyla (Strauss and Lisowski, 2003) All fungi are chemo heterotrophs, requiring organic compounds for energy and carbon. Fungi are aerobic or facultatively anaerobic; no strictly anaerobic fungi are known. The majority of fungi are saprophytes in soil and water; there they are primarily decomposers of materials. Like bacteria, fungi contribute significantly to the decomposition of matter and the recycling of nutrients. Reproduction in fungi occurs by spore formation. These spores, however, are quite different from bacterial endospores ( Tortora, Funke, and Case, 1992). Fungi touch our lives in many ways; they, together with the bacteria, are the decomposers of the world, breaking down vast quantities of dead organic matter that would otherwise accumulate and make the earth uninhabitable. Through their activities, the minerals contained within the dead bodies of plants and animals are made available for recycling through the ecosystem. Their role as decomposers has its dark side, however, because they also cause spoilage of bread, fruits, vegetables, and other foodstuffs, and the deterioration of other goods, fabrics, paper, lumber, and other valuable products. Some fungi are parasitic on or in animals, including humans; many skin diseases, including ringworm and athlete's foot, are caused by fungi and there several serious diseases of the lungs caused by fungi (Mcfadden and Keeton, 1998) Six (6)  Macrofungi are those fungi that form large fructifications visible without the aid of a microscope. This artificial but convenient grouping is hereby defined to include fungal families or genera where the majority of included species produce fruit bodies greater than 1 centimeter in diameter. Unlike micro fungi, which are made conspicuous by the diseases, decay, and moulding they cause, macro fungi are the ones most likely to either to be pollution indicators or threaten beneficial species (Readhead, 1997)

METHODOLOGY
This study was conducted in the island of San Antonio, Northern Samar. The Municipality of San Antonio is one of the 24 municipalities of the province of Northern Samar. The island is geographically located in the western section of the province of Northern Samar. Going to San Antonio takes about an hour and a half travel from Catarman via the Municipality of Victoria by taking the bus going to the cities of Tacloban or Calbayog. From the municipality of Victoria one takes the motorboat to cross the channel to Dalupiri Island, where the municipality of San Antonio is situated. The mainland of Samar is located in the east, in the west is the municipality of Capul, while on the northern part is San Bernardino Strait and on its southern part, the Samar Sea located. San Antonio is composed of 10 barangays, 3 of which are within the town proper. The island is one of the tourist destinations in Northern Samar, because of its beautiful view, fresh air, white sand beaches, and crystal clear water. The shape of this island is elongated; the topography of San Antonio is alluvium-hill of low relief, level to gently sloping. All barangays of San Antonio are situated along the coast. Cebuano is the language spoken by 66% of the residents, while Nenorte Samarnon language is spoken by 34% of the population. The total land area of San Antonio is 2,828,10 hectares (Municipal Planning and Development

Council [MPDC], 2012)
Barangay Vinistahan is a sampling site located at the northern end of the island of San Antonio facing the municipality of Allen. Rice fields and grasslands where different animals are found like goats, carabaos, and cows are prominent land features.
Lagbangan Lake, a man man-made lake, is host to different classes of birds. It can be reached through motorcycle or motorboat. The main income of the residents here is copra production and fishing. The total area is 225.87 hectares (MPDC, 2012).
Barangay Dalupirit is a sampling site located at the western part of the island facing the municipality of Capul. It is a rocky, mountainous area with grasslands used to pasture animals such as cows and carabaos. Coconut plantaion is one of the main sources of income through the production of copra. Another source of income is fishing that contribute much to their livelihood. The barangay can be reached through motorcycle or biking from the town paper. It is considered as the second largest barangay because its total land area is 441.49 hectares (MPDC, 2012).
Barangay San Nicholas is a sampling sites next to barangay Dalupirit, which is more or less 1 kilometer away, and is bounded by the cemetery of Barangay Dalupirit. This place is also a rocky and woody, mountainous area with different species of trees present which are the habitat of birds and other living organisms. Fishing and copra production are the main sources of income of the residents. Its total land area is 190.96 hectares (MPDC, 2012).
Barangay Rizal is a sampling site next to Barangay Nicolas, located also at the western part of the island of San Antonio facingthe municipality of Capul. This barangay is not totally big. An underground cave that contains fresh water is found in an area which is an elevated portion of this barangay. This serves as the sources of water for the residents and is also the source of water for the other barangays through the "Photovoltaic (solar) Pumping System", a project of the Philippine -Australian Community Assistance Program (PACAP) and the Northern Samar Multi-Purpose Cooperative (NSDWCC).
Barangay Manraya is a sampling site located at the northwest part of the island next to Barangay Rizal, facing the Municipality of San Vicente, and is located beside the rocky mountainous area. It can be reached through motorcycle within 15 minutes from the town proper. Fishing and copra production are the main income of the residents and its total land area is 365.16 hectares (MPDC, 2012).
Barangay Pillar is a sampling site located at the southern end part of the island. Rice fields and lots planted to different kinds of vegetables and fruits are situated here because of the good quality of its soil. The government resource conservation project in the municipality, the fish sanctuary, is located here. Fishing, rice plating, and copra production are the main livelihood of the residents, and its total land area is 263.90 hectares (MPDC, 2012).
Barangay Burabod is the sampling site located at the southeast part of the island facing the Municipality of San Isidro, which is mountainous part of the island. Big houses and other establishments are situated here because some residents are businessmen. Fishing and copra production are also the main livelihood of the residents and its total land area is 175.44 hectares (MPDC, 2012). Ward III is located at the south side of the town proper with a road going to Barangay Burabod. Many beach resorts are situated here. The District Hospital is located here and on the other side of the mountain, farms planted to different kinds of vegetables and fruits are located. Fishing and copra production are the main sources of income of the people here, with a total land area of 465.88 hectares (MPDC, 2012).

Ward I, Ward II, and Ward III
During the collection, sample specimens were removed from the substrate using a knife, but before picking the specimen, a digital camera was used to photograph each specimen directly from where it is located. Specimens were handled carefully to avoid damage and destruction of useful identification characteristics. The collected macro fungi species was placed in closed containers so that important features were not lost and they would not dry out. An interview with some of the local residents of the study area was conducted in order to gather information on the economic uses of these macro fungi present in the locality.

Preservation of the specimen
The representative samples of macro fungi were preserved either by drying or by immersing them in a 10% formalin solution and 70% denatured alcohol. Drying under the sun was done immediately for woody macro fungi specimens. For the fleshly fungi which possess the ability of regaining freshness when soaked in water, they were air dried at room temperature; these were not dried directly under the sun because rapid drying disfigures them. Normally, it takes 4 days or, often the time is extended, two weeks of drying under ordinary room temperature.
The method of preserving fragile and soft specimen is by immersing them in 10% formalin solution to make them rigid and firm. After one or two days, they were removed and washed with water and then transferred into jars containing 70% denatured alcohol where they can be kept and preserved indefinitely.

Identification of the specimens
The preserved specimen was sorted for ease of classification and identification. For preferences, some web pages about macro fungi in the internet, books, and other unpublished work were used in the preliminary identification of the collected samples. Verification of preliminary identification was done by an expert on macro fungi species. Available books and references about macro fungi were used to aid the final identification of the specimen that were collected.

Distribution of Macrofungi in the Island of San Antonio, Northern Samar
They were twenty six (26) macro fungi species which were present and distributed in ten (10) sampling sites of San Antonio, Northern Samar. The species composition of macro fungi in San Antonio, Northern Samar is presented in Table  1. A total of 26 species were identified and belonged to two (2) Phyla : Basidiomycota and Ascomycota, five (5) classes: Agaricomycetes, Basidiomycetes,

Kinds of Substrate
The macrofungi in the study were mostly found on dead wood, soil, coconut husk, decaying banana trunks, and tree branches.
Fourteen (14) species of macrofungi were found in dead wood. Eight (8) species were found in soil. One (1) species was found in both coconut husk and in decaying banana trunks and seven (7) species were found in the tree branches. Also, two (2) species were found in all sampling site.

Economic Use of Macrofungi
Based on the interview conducted, there were three (3) species that were locally considered or known as edible by the local residents, namely: Grifola frondosa or "kurakdot', Volvariella volvaceae or "ulaping" and Volvariella gloiocephala or "ligbos", but this three (3) species of macrofungi were not present in identified sites during the sampling of the researchers. The twenty-six (26) species are known and familiar to the local residents but their uses or benefits are unknown.

CONCLUSION
The macro fungi species in the study area were mostly growing on the dead wood, soil, and tree branches, while few of them were growing n coconut husks and decaying banana trunks. The respondents are not familiar with the economic use or benefits derived from the macro fungi species found in the sampling sites.


Applying ex post index decomposition analysis to final energy consumption for evaluating European energy efficiency policies and targets

This paper aims to unravel the effects of both policies and autonomous developments driving the changes of final energy consumption for the European Union (EU28) and its member states complemented with an in-depth analysis of two EU member states, Germany and Poland, for the period of 2000 to 2015 by the logarithmic mean Divisia index methodology (LMDI). We examine the influences on the changes in final energy consumption for each of the five main demand sectors at aggregated level and at a more detailed level of residential end-uses, transport modes and industrial sub-sectors. With the second level, we provide insights into the effects of policies from a European and national perspective. Our analysis shows that final energy consumption in the EU28 is primarily influenced by an increase in energy efficiency in industry followed by households. For the latter, the energy savings were mainly realised in space heating. The main counteracting drivers were increasing economic activity and the rising demand for higher comfort and social factors, such as a declining household size. Germany shows relatively low energy efficiency improvements in industry, but strong energy efficiency gains in households followed by transport. Poland’s transport, however, is responsible for a strong increase in final energy consumption mainly through increased activity, while here the sector industry—despite strong effects of increased activity—in total shows a significant reduction in energy consumption through energy efficiency improvements.

Introduction
The final energy consumption in the European Union of 1062 Mtoe in 2014 was a record low in the period since 1994 and already surpassed the 20% energy efficiency objective of 1086 Mtoe targeted for the year 2020 (Bertoldi et al. 2016). This target is measured against the business-as-usual scenario (BAU) of PRIMES 2007 (Capros et al. 2008) (Fig. 1). In 2015, the total final consumption slightly increased to 1082 Mtoe, practically coinciding with the 20% objective and reaching the same level as 1995. The updated targets of the EU's 2030 Energy Strategy require at least 27% energy savings compared with the same BAU scenario (European Commission 2014) and the present proposal of the EU Commission is even aiming at 30% energy savings (European Commission 2016a). Based on the energy statistics of 2015, the gap to be closed in the European Union for reaching the 2030 objectives of 20% and 30% are below 6% and 10%, respectively.
It should be noted that the PRIMES 2007 reference scenario is a baseline developed before the economic crisis of 2008 and has been maintained by policy makers to ensure consistency of progress tracking over time. This baseline has been corrected successively during each new projection, which occurred every 2 to 3 years (see for example PRIMES 2012 (Capros et al. 2013) and PRIMES 2016 (Capros et al. 2016) baselines in Fig. 1), which are considerably lower than the original PRIMES 2007 baseline.
Most of the total reduction of 4.5% between 2000 and 2015 was due to the industry sector with a reduction of almost 18% (− 1.3%/a) followed by the sector agriculture/forestry/fishing with − 16% (− 1.2%/a). This development was counteracted by a rising energy consumption in the sector services (+ 21%). The final energy demand between 2000 and 2015 shows two distinct phases: up to 2006 final energy was still increasing, while it has been experiencing a decreasing trend since then. The even higher reduction of almost 8% in the period of 2007 to 2015 is partly due to the decrease of final energy consumption of the transport sector by 6.5%.
To identify the key determining factors for these changes in final energy consumption and their impact, we conducted an index decomposition analysis (IDA) for final energy consumption (FEC) of all member states of the EU28. We focused on 2000 to 2015, as this period shows a good statistical coverage for all EU member states and is relevant for a number of energy efficiency related policies in the EU. In some interesting cases, we also consider the period of 2007 to 2015 to analyse the development directly following an economic recession. The most relevant policies related to energy efficiency in these periods are as follows: At the same time, there are a large number of national energy efficiency policies, which also contribute to improving energy efficiency. For an overview, see the ODYSSEE-MURE database (ODYSSEE-MURE 2017) on energy efficiency policies and trends.
Some of these policies directly affect final energy consumption, while others such as the EU-ETS (the effort sharing decision and the industrial emissions directive) only indirectly affect the demand side. These mostly aim to reduce emissions such as carbon dioxide and other GHG or harmful gases to protect the environment and human health due to energy conversion or industrial activity.
In this paper, we examine developments of final energy consumption in the European Union and its member states, by considering those factors which had a strong impact on final energy consumption by decomposing energy use on the demand side by sectors and subsectors, transport modes and enduses.
Index decomposition analysis is commonly used for primary or final energy consumption (e.g. Wang et al. (2014),  and Song and Zhang (2017) for final energy consumption in China; Timma and Blumberga (2014) for the energy sector in Latvia; Colinet Carmona and Román Collado (2016) for energy consumption in Andalusia; Stenqvist (2015) for the energy consumption of the Swedish pulp and paper industry; and Kerimray et al. (2018) for energy intensity in Kazakhstan) and GHG emissions in individual countries or regions Wachsmuth and Duscha 2018;Kopidou and Diakoulaki 2017;Wang et al. 2017;Xu et al. 2017;Karmellos et al. 2016;Xu et al. 2016a, b;Colinet Carmona and Román Collado 2016;Moutinho et al. 2016;Nie et al. 2016;Pardo Martínez and Silveira 2013). Recently, also different aspects of air pollution other than GHG emissions were studied using index decomposition (e.g. Xu et al. (2019) and Li et al. (2018)).
Past studies using index decomposition analysis on energy consumption often considered only the final energy consumption of one single sector, such as industry, households or transport focussing on one single country (e.g. Holzmann et al. (2013) residential in Austria, Chong et al. (2017) for residential in the Guangdong Province, Zhang et al. (2016) and  for the residential energy consumption in the Jiangsu Region and Shandong of China; Achour and Belloumi (2016) transport in Tunisia or China (Zhang et al. 2011) and Kim (2017) on manufacturing in Korea). The sectors agriculture, services, transportation and industry in Turkey are analysed by Yilmaz and Atak (2010). Marrero and Ramos-Real (2013) analysed the changes in energy intensity in the EU15 from 1991 to 2005. Fernández González et al. (2014 studied the aggregated final energy consumption in the EU27. For an ex ante analysis, Smit et al. (2014) examined the underlying effects of the developments in the projections of PRIMES 2007 and PRIMES 2013. Xu and Ang (2014) give an overview of several studies analysing final energy consumption in the residential sector listing 20 publications from 1997 to 2013.
A comprehensive analysis of the development of final energy consumption and its driving factors on a sectoral level can function as an overarching analysis providing additional insights.
In this paper, we aim to provide such analysis to show the effects of energy policies in the European context and on a national level. We try to answer the question of what influence different selected drivers have on final energy consumption in the demand sectors and how strong the effects are. We also try to link these developments to the main energy and climate policies of these sectors and to derive policy recommendations.
To the best of the authors' knowledge, so far in other recent studies, this has not been done for the European Union as a whole and its member states for all relevant demand sectors in a uniform methodological framework.
In Reuter et al. (2017), we analysed the effects underlying the changes in primary energy consumption in the EU28 and its member states. We found that the reduction of final energy consumption played a major role in decreasing primary energy consumption compared to other factors, explaining approximately 65% of the total reduction. In contrast, other developments, e.g. increased renewable energy in the energy sector, contributed relatively little. This is a remarkable outcome also in view of the predominating understanding according to which energy efficiency policies have so far not been sufficiently effective. It is thus essential to better understand the developments on the energy demand side and to analyse the developments underlying the changes in final energy consumption, which is the topic of this paper.
The value added of our work lies in the comprehensive analysis of all sectors of the demand side affecting the final energy consumption within one comprehensive methodological framework and based on a consistent data corpus. In addition, this study comprises all member states of the European Union, shedding light on the underlying developments of the region as a whole. We are therefore convinced that the present work represents a novelty and offers clear added value to past studies as these only consider a single country or demand sector.
This analysis and the results may be useful for the design and implementation of future energy policy as policy makers are currently facing crucial decisions regarding the achievement of targets set for the near and distant future in the European Union and globally.

Decomposition method
For analysing the underlying effect of changes in the final energy consumption, in this study, we use the index decomposition analysis (IDA) based on the socalled logarithmic mean Divisia index method (LMDI), which is widely adopted. Different methods were used over time and have their respective advantages or disadvantages.  give an overview of studies implementing decomposition analyses using LMDI in recent years. Ang and Zhang (2000) and Ang (2004) assessed several IDA methods, recommending the LMDI I method due to its theoretical foundation, adaptability, ease of use and result interpretation. LMDI provides a Bperfectd ecomposition, i.e. not giving a residual term, which would complicate the interpretation of results. Ang et al. (2009) compare different popular decomposition methods and emphasise that for a large number of factors the advantage of simplicity of use becomes particularly evident, which is useful in our analysis.
As a common use case of IDA, the impacts of variations in overall activity level (e.g. gross value added), activity structure and energy intensity on total energy consumption in a specific sector (mostly industry) are analysed.
The additive form of LMDI approach follows this general formula: Here, V describes an aggregate (e.g. final energy consumption) or a sub-aggregate i (e.g. an industry sector or a mode of transport) which is composed of different factors x and its changes in the time period 0 to T for which the decomposition is conducted (see Ang 2005). L (a, b) describes the logarithmic mean of two positive real numbers-in this case, different aggregates of final energy consumption of the first 0 and last year T-a and b, which are used as the weighting function in LMDI I.
The decomposition of changes in total final energy consumption FEC according to Eq. (1) can be formulated as follows: where the factor ΔFEC ACT for the sector industry as an example would be constructed as follows (and in analogous manner for other sectors): with ACT T and ACT 0 describing the activity at a given point in time for industrial sector i as defined in Eq. (1) (see Ang 2015). Furthermore, the factor ΔFEC STR describes the changes in final energy consumption due to structural changes in industry; ΔFEC MOD represents the changes attributed to shifting transportation modes; and ΔFEC COM stands for changes due to variation in comfort and social factors, such as living space and inhabitants per dwelling. ΔFEC HDD makes influences of changes in average temperatures of a country on the energy consumption for households visible, while ΔFEC EFF provides information about the changes in final energy consumption due to improvements of energy efficiency in all sectors.
As the LMDI I method is not defined for zeros or negative numbers in the data set (due to logarithmic terms in the formulae), it is necessary to substitute these to avoid errors in computation. Two main strategies for this exist, the so-called analytic limit strategy as described by Wood and Lenzen (2006) and the small value strategy introduced by Ang and Choi (1997). The LMDI I method only results in insignificant errors if applied to a data set containing a large number of zeroes or small values. Since this is not the case for our analysis, we used the latter strategy to handle zero values and substitute those with values smaller than 10 −20 .

Definitions
Final energy consumption (FEC) is defined as the energy supplied to the final consumer, which is directly converted to useful energy in several end-uses, like mechanical energy or heat. It is distinguished from final consumption, which also includes non-energy uses (IEA 2004).
In this paper, we distinguish five sectors and their corresponding final energy consumption (see Fig. 2).
To compose the factors for the total final energy consumption of a country, we aggregate the factors from the sectoral level. This aggregation is depicted in Fig. 3.
In the industry sector (IND), we consider the following three underlying factors: These factors are defined as follows: where i describes the sub-sector. In our analysis we consider the main industry sectors with their subsectors (section C in the European classification NACE for industrial activities) in Table 1, excluding sections D (electricity, gas, steam and air-conditioning supply) and E (water supply, sewerage, waste management and remediation). For the sector Bhouseholds^(HH), the approach is designed based on final energy uses in that sector. In total, we consider up to five different factors, depending on the end-use, which are shown in Table 2.
For each of the following end-uses, a separate formula is used for the decomposition: -Space heating -Hot water -Electric appliances and lighting where the factor ACT measures the impact of population on the changes of final energy consumption. SOC (defined as dwellings per capita) shows influence of household size (persons per dwelling) on energy consumption, while the factor COM shows the influence of the size of dwellings, i.e. comfort of living. The factors INT and CLI measure the influence of changes in efficiency in households and the impact of changes in weather on the space heating consumption. For the latter effect, see more the more detailed discussion further on. For the total sector households, the different factor on the end-use level are aggregated as shown in Fig. 4.
The final energy consumption of the sector transport is composed of the factors activity, modal shift and energy efficiency. These are calculated separately for passenger and freight transport using the following formulae: where the factors ACT and ACT i show the impact of changes in transport performance measured in passenger or tonne-kilometres of a transport mode i (i.e. road, rail) or overall. MOD describes the changes due to modal shift, i.e. the switch from or to road transport such as cars and lorries. INT i gives information on the changes in energy efficiency, i.e. energy consumption per passenger or tonnekilometre of a mode i. These factors are analysed for both freight and passenger transport. For the sectors services (SER) as well as Bagriculture, fishing + other^(AGR), the final energy consumption is decomposed into two factors, the effect of the number of employees and of energy efficiency (specific energy consumption per employee) (see Formula 11). For the service sector, despite its importance, there is no coherent data set on energy end-uses available covering the whole of the European Union. Thus, a factor showing the influences of the climate unfortunately is not available for the sector services.
Data sources To obtain detailed insight into the effects of each sector and thus allow more in-depth analysis, we use a combination of three data sources, namely the ODYSSEE database 1 , Eurostat and the statistical pocketbook by DG TREN (European Commission 2016b). For the energy demand data, we used publicly accessible data for the EU28 and its member states from the European Central Statistical Office Eurostat. Our database was compiled with bulk data from the tables BSimplified energy balances^ (table code nrg100a), BSupply, transformation and consumption of electricity( nrg105a) and BSupply, transformation and consumption of heat^(nrg106a). 2 Eurostat data are, however, not detailed enough to carry out the analysis at a sufficient level of depth. We therefore for this analysis further used data compiled by the ODYSSEE-MURE project (ODYSSEE-MURE 2017) for the aspects listed in Table 3.
For some special data series on transport, we also used data from the statistical pocketbook of the Directorate General for Mobility and Transport (DG TREN 3 ).

Data gaps and imputation
There were a few missing data points in the ODYSSEE data. Those were filled with other data source such as national balances, DG TREN or Eurostat data, where suitable. Most countries are covered by the ODYSSEE database to a large degree. For a small number of countries, the data on gross value added for the year 2015 was not completely available from neither Eurostat nor ODYSSEE, which is why these gaps are filled with the help of production indices, as they reflect economic activity sufficiently accurately.
For Malta and Luxembourg, data show several gaps in a significant number of data series. For this reason, and since those member states do not significantly contribute to the consumption of energy in the European Union, we omitted them in our analysis. Also, an extensive data imputation is always a source of error and can heavily affect the results of the analyses for individual countries.

Correction of annual climate/weather conditions
While effects of changes in annual average temperature play a minor role in sectors like industry and transport, energy consumption of households and services is highly influenced by climate and changes in weather. To account for the influence on final energy consumption by temperature, we demarcate an own effect in the households sector as Bannual climate variation (weather)^. This effect links the changes in heating degree days to the share of change in final energy consumption in the respective sector. For the service sector, this correction could not be made as there is no reliable breakdown of end-uses available for all countries.

Results
While the analysis was carried out for the European Union as a whole and most of its member states (except Malta and Luxembourg), we will here additionally focus on two countries besides the EU28. We selected Germany and Poland for a detailed comparison, as they represent good examples of countries whose energy consumption is developing on different levels of advancement towards the European Union target. Germany represents a country where the final energy consumption has reached a certain saturation level (variation of ± 6% within the last 15 years) and economic activity shows a steady growth. At the same time, Germany's final energy consumption still did not reach the target of 194.3 Mtoe proposed by Germany in the context of the EED for 2020 and faces a gap of 9.4%. On the other hand, Poland represents a state which is still expected to enjoy strong economic growth and has never exceeded its 2020 target proposed in the frame of the EED. Furthermore, Poland shows large potential of gains in energy efficiency in industry and other sectors, while Germany's most energy-intensive sector industry partially reached a saturation of process efficiency in recent years.
Results for all member states and the European Union are shown in Table 4 (Appendix).

Results for the European Union
Total final energy consumption in the European Union declined by about 48 Mtoe in the time period 2000 to 2015 (see Fig. 5). Most of this reduction was realised in the sector industry with − 59 Mtoe. This trend was counteracted by the developments in transport (+ 14 Mtoe) and services (+ 17 Mtoe). The households sector contributed with a reduction of final energy consumption of 16 Mtoe.
Activity levels of the various sectors accounted for the largest increase of about 125 Mtoe, followed by changes from comfort/behaviour, social factors and climate effects in household consumption, which contributed 35 Mtoe and 6 Mtoe, respectively. Modal shift in transport had only a minor increasing effect of about 2.6 Mtoe. The main decreasing effect-counteracting all of the increasing effects-is contributed by efficiency gains in all sectors with 210 Mtoe. Structural changes contributed in a minor manner to the decrease with 6.6 Mtoe. The main share of the efficiency gains occurred in industry (− 85 Mtoe) and households (− 69 Mtoe), followed by transport (− 43 Mtoe) and others (− 12 Mtoe) (see Fig. 5).
While comfort and behavioural effects in households, driven by the size of dwellings and persons per dwelling, had an increasing effect on energy consumption, improved efficiency, i.e. lower energy consumption for space heating per square meter of dwelling area, had a major reducing impact of − 59 Mtoe, while the total efficiency gains in households (including appliances) had an impact of − 69 Mtoe. These efficiency gains were mainly realised through more efficient space heating technologies and retrofitting of building envelopes decreasing the specific consumption by about 30% in the period of 2000 to 2015 resulting in a strong reduction of final energy consumption. This is largely due to the EPBD and its national implementations in performance standards. Retrofit schemes also have a large impact on the specific energy consumption of buildings (Webber et al. 2015). The efficiency gains in electric appliances in total played only a minor role, The comfort/behaviour/social factors and the weather factor had a strong increasing effect, with 35 and 6 Mtoe, respectively. The comfort effect is most pronounced for the end-use of space heating as it is directly coupled to the increase of dwelling size, which grew by over 6% in the EU from 2000 to 2015. Overall, the combined effects led to a decrease of final energy consumption by 16 Mtoe in households (see Fig. 6). In industry, the increase in activity, expressed by gross value added had an increasing impact on energy consumption of 33 Mtoe. Structural changes in the sector industry, i.e. changes of the composition of the sector across the different sub-sectors led to a decreasing effect of around 6.6 Mtoe (see Fig. 8). Efficiency gains in the sector resulted in a decrease of 85 Mtoe dominating all other effects. Overall, the sector industry played a The main share of the efficiency gains was realised in the sub-sectors Bprimary metals^(− 23.9 Mtoe) and Bchemical and petrochemical^(− 24.2 Mtoe).
Besides autonomous progress in energy efficiency, this can be assumed to be the result of successful policies both at the European and at the national level. Particularly, the Emission Trading Scheme, the Ecodesign and Labelling Directive as well as the introduction of energy audits and energy management systems as result of the EED are likely to have played an important role at the European level. In addition, it is plausible to assume that national programs like the subsidy programme on energy efficient cross-cutting technologies as part of the National Action Plan on Energy Efficiency (NAPE) in Germany had a considerable impact on energy efficiency at the national level.
In the transport sector, total final energy consumption decreased by 14 Mtoe between 2000 and 2015 (see Fig. 9). This total change is composed of an increasing effect of almost 55 Mtoe due to rising activity levels in passenger transport (passenger-kilometres + 8%) as well as freight transport (tonne-kilometres + 9%), modal shift towards more road-based traffic (+ 2.6 Mtoe) and energy efficiency (− 43.3 Mtoe). This shows that public policies promoting modal shift so far had at best the impact to slow down the trends towards road-based transport. The modal shift mainly occurred in freight transport, where a slight shift from rail transport to road transport took place. Gains in energy efficiency in passenger transport mainly occurred in rail bound traffic by decreasing specific consumption by 30%, while specific consumption in road passenger transport decreased by only 8% in the same period. These efficiency improvements resulted in − 1.14 Mtoe (rail) and − 16 Mtoe (road). This indicates that European transport policies aiming at technological improvements, such as CO 2 standards for cars and vans, car labelling, as well as national policies such as bonus/malus schemes in France, had some impact in the period considered, but not yet enough to curb the transport need (Ministère de la Transition écologique et solidaire 2018).
Compared to the period 2000-2015, the energy efficiency in transport played a bigger role in the period of 2007 to 2015 and outpaced all other factors (see Fig. 10) resulting in a total change of − 25 Mtoe. A relatively small rise in activity had an effect of about + 6 Mtoe, while modal shift had a reducing effect of 1.5 Mtoe.
The changes of the service sector are mainly driven by rising activity levels (number of employees): plus 19% regarding the number of employees. This resulted in an increasing effect of almost 25 Mtoe. In comparison, energy efficiency in this sector played only a minor role, with a decrease by 8 Mtoe in the time period from 2000 to 2015. This changed in the period after 2007, where energy efficiency improved more strongly in the service sector, resulting in a decrease of energy consumption per employee by almost 1.5%.
Overall, the final energy consumption in the service sector in the EU28 rose by almost 17 Mtoe from 2000 to 2015. However, this development was different in the period of 2007 to 2015 as the final energy consumption of the service sector dropped by about 2.2 Mtoe (see Fig. 11). This difference in development from 2007 to

Results for Germany and Poland
As discussed above, the changes in final energy consumption at the European Union level were primarily driven by energy efficiency improvements in the industry sector followed by households. As an in-detail comparative example, we here show the results for two countries. On the one hand, Germany, as a representative for countries in the EU which have reached a certain saturation level regarding their energy consumption. On the other hand, Poland, which stands for new member states of the EU, which are expected to show significant economic growth and thus increasing energy consumption in the future.
The changes in final energy consumption in Germany are mainly driven by energy efficiency gains of almost 38 Mtoe in the period 2000-2015 (Fig. 13). These were counteracted by rising activity levels causing an increase of 26 Mtoe. Comfort/behaviour and social factors in the residential sector contributed an increase of around 7 Mtoe, while climate variation is found to contribute an increase of almost 2 Mtoe. Modal shift in the transport sector only had a small effect of below 1 Mtoe on the change in final energy consumption.
The changes in the period of 2007 to 2015 show a similar picture. However, due to smaller energy Poland's final energy consumption underwent similar changes to Germany's in the period considered (Fig. 14). Activity changes amounted to a total increase of 19 Mtoe and were only slightly smaller than in Germany, while Poland's final energy consumption represents only one third of Germany's. These increasing activities occurred mainly in industry and in transport. Structural changes in industry reduced total final consumption by about 3 Mtoe. Modal shift had a larger impact compared to Germany with about 2 Mtoe (i.e. a much stronger shift to roadbased transport). Comfort/behavioural and social factors (i.e. increase in persons and area per dwelling) had an effect of + 3 Mtoe. Changes in climate/weather had a more marginal effect, resulting in an increase of less than 1 Mtoe. Increasing energy efficiency had one of the biggest impacts with almost − 16 Mtoe.
In the time period of 2000 to 2015, the gross value added of Germany's industry increased by about 20%. This rising activity of industry had an increasing effect of nearly 11 Mtoe (see Fig. 15) Energy Efficiency and was mostly realised in the sub-sectors Btransport equipment^(+ 83% GVA from 2000 to 2015) and Bchemical and petrochemical^(+ 32%). Structural changes in the sector towards less energy intensive sub-sectors mainly at the expense of the sub-sectors Bprimary metals^and Bnon-metallic minerals^reduced final consumption by about 5 Mtoe. An increase of 14% in overall efficiency in industry resulted in a decrease of 3 Mtoe. These gains in energy efficiency mainly were realised in the sub-sectors Btransport equipment^and Bprimary metals^. In primary metals, this was partly due to a shift towards more recycled steel. All these effects combined led to an increase of final energy consumption by 3.4 Mtoe. This contrasts with the development at the EU level where industry contributed most to the efficiency gains.
Poland's industry increased its gross value added by 83% in the time period considered. This resulted in an increasing activity effect of about 10 Mtoe (see Fig. 16). Structural changes decreased final energy consumption by 3 Mtoe, while the energy efficiency gains contributed with a reduction of almost 11 Mtoe. These gains were mainly realised in the sub-sectors Bchemical and petrochemical^and Bnon-metallic minerals^. All effects combined led to a total decrease of 3.4 Mtoe in final energy consumption of industry. In contrast to Germany, Poland realised large improvements in energy efficiency in the period considered. Reasons for this difference lie in the fact that Germany already restructured its industry in the 1990s and that the energy efficiency level of Poland was much lower than in Germany before 2000; hence, larger improvements were possible.
As shown in Fig. 17, the changes in final energy consumption in the sector households in Germany were mainly driven by rising energy efficiency (− 21 Mtoe) due to improvements regarding the specific energy consumption for space heating (− 38% from 2000 to 2015). Effects, such as increasing size of dwellings, decreasing household size as well as increasing equipment with electric appliances and lighting, counteracted this decrease. These comfort, behavioural and social developments led to an increase of 7.3 Mtoe, while changes in weather had an effect of + 1.8 Mtoe. Germany's slightly deceasing population Households' final energy consumption in Poland shows a similar structure in changes as in Germany (see Fig. 18). However, energy efficiency improvements are smaller than in Germany as the specific energy consumption for space heating decreased by only 23% in the time period considered. This results in a decrease by 2.6 Mtoe attributed to energy efficiency, while this was counteracted by weather effects and rising comfort T h e c h an ge s i n en e rg y c on s um pt i on i n Germany's transportation sector were mostly driven upwards by a rise of activity regarding road transport in both passenger and freight transport, with + 14% and + 19%, respectively. Overall activity of the sector rose by 13% for passenger and 15% for goods transport. The shift between the modes of transport only account for a change of 0.21 Mtoe, while the improvement of energy efficiency in transport resulted in a decrease of around 13 Mtoe (see Fig. 19). The main share of these improvements was realised in passenger transport as here the total energy intensity dropped by 24%, which is mainly due to more efficient road transport followed by rail and domestic air transport. All effects combined resulted in a reduction of final energy consumption by 3.7 Mtoe.
In the period from 2007 to 2015, the changes in the transport sector show a similar pattern. While the gains in energy efficiency (− 1.77 Mtoe) were smaller, the effect of modal shift was slightly larger (− 0.32 Mtoe). Rising activity in transport contributed an effect of + 2.8 Mtoe resulting in a total change in final energy consumption by 0.7 Mtoe.
The changes in energy consumption in Poland's transportation sector show a similar pattern (see Fig. 20). These were driven upwards by 7.3 Mtoe through a rise of activity regarding road transport in both passenger and freight transport, with + 29% and + 162%, respectively. Modal shift had a larger impact with over 2 Mtoe, indicating a strong shift towards road transport. Unlike in Germany's transport sector, energy efficiency in Poland had a smaller effect and resulted in a decrease in final energy consumption by 2 Mtoe. This is a minor effect mainly due to a decreasing efficiency regarding person-kilometres in passenger transport in general (+ 12%) and road transport in particular (+ 6%), which counteracts efficiency gains in freight transport (− 10%). As energy efficiency could not counteract the increase in activity, the total change led to an increase in total final energy consumption with 7.3 Mtoe. The reason for the degradation of the efficiency in passenger transport is the shift towards larger cars.
For the period from 2007 to 2015, a similar development can be observed. However, while the impact of rising activity of the sector is only slightly lower (5 Mtoe), the effect of energy improvement in transport is relatively high (− 4.3 Mtoe) compared to the total period. Together with a slightly smaller effect of modal shift (+ 1.2 Mtoe), the total effect adds up to about 2 Mtoe. The service sector in Germany increased its final energy consumption in the period from 2000 to 2015 by almost 5 Mtoe (see Fig. 21). This was mainly due to the increase of activity by 17%, which led to an increase of about 5 Mtoe. At the same time, energy intensity in the sector dropped slightly by about 1% resulting in a decrease of 0.2 Mtoe.
In the time from 2007 to 2015, the energy consumption of Germany's services sector rose by 3.9 Mtoe. This was mainly due to an increase of activity by 10% resulting in an effect of + 3 Mtoe, while a decrease in energy efficiency of 3% added 0.9 Mtoe.
Changes in Germany's agricultural sector were only partly driven by decreasing activity and more by (apparently) decreasing efficiency in the sector. The decrease of activity in form of gross value added by 14% contributed about 0.6 Mtoe to the total change of 2.8 Mtoe. An energy efficiency decrease of 10% resulted in an increase of 1 Mtoe (see Fig. 21).
In Poland's service sector, the increase in final energy consumption due to additional activity of + 19% was smaller and resulted in an increase by 1.1 Mtoe (see Fig. 22). At the same time, the energy intensity of the sector increased rapidly by 31%, which resulted in almost Energy Efficiency 2 Mtoe. All these effects add up to a total increase of the final energy consumption of 2.8 Mtoe or 56%. The reason for the increasing energy intensity is not a loss in efficiency but rather due to larger equipment of service sector buildings with energy consuming devices.
In the period from 2007 to 2015, energy consumption in the sector increased by 10%. The activity grew by 12%, while energy intensity dropped by 1.7%. These developments resulted in effects of + 0.8 and − 0.12 Mtoe, respectively. The total change in services was + 0.8 Mtoe. The fact that energy intensity was decreased after 2007 compared to the period since 2000 may be due to the impacts of thermal building standards.
Final energy consumption of Poland's agriculture was driven by increasing activity of over 12% and strong improvements of energy efficiency by 37% in the sector. Growing gross value added contributed 0.5 Mtoe, while energy efficiency gains resulted in a reduction of about 1.8 Mtoe. Overall, the final energy consumption of Poland's agriculture decreased by almost 1.3 Mtoe.

Discussion
Overall, the final energy consumption in the European Union decreased by about 50 Mtoe in the period from 2000 to 2015. Most of this decrease is due to efficiency gains in all sectors, which more than counteracted all increasing effects (see Fig. 23). The largest share of these efficiency gains was realised in industry (− 85 Mtoe) followed by the households sector (− 69 Mtoe). In households, the main energy efficiency gains resulted from more efficient space heating (technologies) with a decrease by − 60 Mtoe. In industry, most changes occurred in the sub-sectors Bprimary metals^and Bchemicals and petrochemicals^. To conclude, the changes in the industry sector were found to contribute substantially to the reduction of final energy consumption in the EU28, resulting in a decrease of its total final energy by almost 18% between 2000 and 2015.
This development can be assumed to be linked to the Energy Efficiency Directive, which includes several measures aiming at energy efficiency in industry, such as the implementation of energy audits and energy management systems (Article 8 EED).
The EU15 4 countries show a reduction of energy consumption due to increasing energy efficiency in industry. The exception in the general development in this group is Austria, for which energy use increased due to decreasing energy efficiency especially in the sub-sector Bprimary metals^.
New member states show a similar trend, while the gains through energy efficiency are compensated for by increasing activity in the period from 2000 to 2015, which draws a similar picture to Fernández González et al. (2013) for the period from 1995 to 2010.
The energy use of households in EU15 countries showed relatively small decreases or even slight increases (e.g. Italy). Energy efficiency improvements in households are reduced by increasing comfort (i.e. larger dwellings) and population growth, which Holzmann et al. (2013) also observed for final energy consumption in Austria. Italy shows a reduction in energy efficiency in households, which combined with increasing population and comfort levels led to a strong increase of final energy consumption in households. However, overall in the European Union energy efficiency plays an important role in the development of energy consumption in households.
This can be assumed to be linked to the strong energy efficiency policies in the sector; the effects of which are weakened mostly by the increase of comfort levels and equipment rates as our analysis shows. Namely, these are the Energy Performance of Buildings Directive (2010/31/EU) and the Ecodesign Directive (2009/125/EC).
Unlike the other EU member states, Sweden shows a decreasing effect of comfort/behaviour/social factors due to an increasing number of persons per household, which counteracts the increase due to the rise in comfort (larger dwellings).
The largest absolute impact on the reduction of final energy consumption in the period from 2000 to 2015 is contributed by the UK with − 21.4 Mtoe (− 14.3%), followed by France (− 11.1 Mtoe, − 7.2%) and Italy (− 8.4 Mtoe, − 6.6%). These countries also have realised the highest improvements in energy efficiency in industry, which contributed most energy savings. These improvements were mainly realised in the sub-sectors Bprimary metals^and Bchemicals and petrochemicals^, which are also identified by Bertoldi et al. (2018) as the subsectors with the most energy efficiency progress in industry. Poland shows the highest increase in final energy consumption with + 7 Mtoe (+ 12.8%) followed by Austria (+ 4 Mtoe, + 15.5%) and Lithuania (+ 1 Mtoe), which also shows the highest relative increase in final energy consumption in the EU (together with Malta; both + 29%). In these countries, increased energy consumption in transport has been responsible for the increasing energy consumption due to a rising activity in the sector.
Transport had a significant increasing impact on the energy consumption in the European Union as a whole. The main reason for this development is the strong growth in the activity of both passenger and freight transport, in particular regarding road transport and aviation. While efficiency gains weaken this development, they cannot compensate for the growth in activity in this sector. Most of these efficiency gains were realised in only a few countries, namely Germany, Spain, France and the UK.
It can be assumed that the results we produced are accurate within the limits of the method, because the data used is highly consistent. If this consistency were lower, the results could have been distorted due to the properties of the LMDI method regarding inconsistencies in underlying data.
Furthermore, very few missing data points in a large data set were replaced, so that this imputation has only a small influence on the overall quality of the results.

Conclusions
We decomposed the changes in final energy consumption of five demand sectors using the logarithmic mean Divisia index method for the EU28 and its member states.
The developments observed imply several possible policy recommendations to steer the future development of final energy consumption in the European Union towards the fulfilment of the targets set for 2020 and 2030.
Compared to the other sectors, the transport sector is particularly predestined to be targeted by future energy efficiency policies as here only small energy savings were realised, especially in the new member states of the European Union. Our analysis shows cumulated energy efficiency gains of 32 Mtoe, while the increase in activity in the member states adds up to 40 Mtoe.
Thus, energy efficiency in transport should be targeted by an increased policy intensity to counteract rising activity and leading to the further reduction of final energy consumption in this sector. This is especially the case for new member states, e.g. Poland, showing an increase of 74% in the period from 2000 to 2015. At the same time, the sector transport has so far received only little attention from energy efficiency policies in the European and national context.
In other sectors, a large degree of the activity changes are compensated for by increases in energy efficiency and changes in the sector structure in industry.
As shown in our previous analysis of the changes in primary energy consumption using IDA, the decrease in final energy consumption is a major factor for reducing primary energy consumption of the European Union besides the penetration of renewable energies (Reuter et al. 2017). The analysis of final energy savings presented in this paper offers insights, which can help to design strategies for target achievement in a European and international context.
Our analysis leads to interesting topics for future research. For example, an in-depth analysis of individual sectors and countries, which could not be included here due to the limitations of the method and data, would be an interesting option. For this purpose, the households and transport sectors would be of particular interest.
For the former, an investigation of the effects in households affected by energy poverty would be appropriate. To achieve the ambitious goals in the building sector, this group and its specific needs must not be ignored. Thus, future policies regarding energy efficiency in buildings should also include specific elements aiming at the special barriers that particularly affect this group.