Prediction of household and commercial BMW generation according to socio-economic and other factors for the Dublin region

Assessing the implementation effect of Shanghai's compulsory municipal solid waste classification policy

Waste treatment is a problem faced by cities all over the world. In recent years, China, as a developing country, regards the municipal solid waste (MSW) classification as one of the important strategies to deal with the MSW problem. The previous MSW classification policies in China were all only advocacy in nature. It was not until January 2019 that the "Regulations on the Management of MSW in Shanghai" was officially promulgated as China's first compulsory MSW classification policy, marking the beginning of an era of compulsory MSW classification in China. How effective is the implementation of Shanghai's compulsory MSW classification policy 18 months after its implementation and can developing countries continuously and effectively implement compulsory MSW classification policies? These are important issues of concern to the government, academia, and the public. This paper establishes a three-stage DEA model to evaluate the implementation effect of the compulsory MSW classification policies in Shanghai during the period of February 2019 and July 2020. The study found that the average efficiency of the compulsory MSW classification policy in Shanghai reached 0.906 during the study period, indicating that the policy was executed reasonably well. However, there are only 5 months in 18 months that the policy was fully effective (reaching efficiency level 1), suggesting that there is still room for improvement. The main reason for not being able to achieve full effectiveness in some months is attributed to scale efficiency. At the same time, the general public budget revenue and expenditure of environmental variables have positive and negative impacts on the policy implementation effect in Shanghai. The research results can provide experience for China to comprehensively implement the compulsory MSW classification policy in the future and can also provide valuable case study information for cities in other developing countries to implement the compulsory MSW classification policy.

Does living alone increase the consumption of social resources?

China's living arrangement has changed as the economy grows and society makes progress. More and more people prefer to live alone. In 2018, a total of 240 million people chose to live alone in China, with an annual growth of 0.53% per year. How will the growing number of people living alone affect the resources consumption and the ecological environment? Based on the data from 1998 to 2017 at provincial level, this paper selects domestic water and electricity consumption to represent resources consumption, and household garbage generation to represent ecological environment, taking the proportion of single-person households in the total households as the explanatory variable and age, education, and household appliances as the control variables. This paper aims to apply dynamic panel models to analyze the impact of solitary population on resources consumption (water and electricity resources as representatives) and on waste generation. The results show that (1) people living alone consume more resources and generate more garbage, while household waste is influenced most, followed by household electricity consumption and household water consumption, (2) positive relations between age and resources consumption and waste generation have been identified, and (3) the energy-saving technology of home appliances is conducive to resources conservation and emission reduction.

Recycling behaviour: Mapping knowledge domain through bibliometrics and text mining

In recent years, the research on human behaviour in relation to waste management has increased at an exponential rate. At the same time, the expanding academic literature on this topic makes it more difficult to understand the main areas of interest, the leading institutions and authors, the possible interconnections among different disciplines, and the gaps. This paper maps knowledge domain on recycling behaviour through bibliometric analysis and text mining in order to identify current trends, research networks and hot topics. 2061 articles between 1975 and 2020 from three different databases are examined with an interdisciplinary approach. The findings reveal that 60% of papers have been published between 2015 and 2020, and this topic is of global interest. Leading countries are mainly located in Europe, North America and Commonwealth; however, China and Malaysia are also assuming a driving role. Bibliometrics and text mining provide the intellectual configuration of the knowledge on recycling behaviour; co-word analysis individuates conceptual sub-domains in food waste, determinants of recycling behaviour, waste management system, waste electrical and electronic equipment (WEEE), higher-level education, plastic bags, and local government. Overall, waste management and related human behaviour represent a universal challenge requiring a structured and interdisciplinary approach at all levels (individual, institutions, industry, academia). Lastly, this paper offers some suggestions for future research such as smart city design, sensor network system, consumer responsibilisation, the adoption of a more comprehensive view of the areas of investigation through the holistic analysis of all stakeholders.

Comparative performance analysis of support vector regression and artificial neural network for prediction of municipal solid waste generation

The evolution of machine learning (ML) algorithms provides researchers and engineers with state-of-the-art tools to dynamically model complex relationships. The design and operation of municipal solid waste (MSW) management systems require accurate estimation of generation rates. In this study, we applied rapid, non-linear and non-parametric data driven ML algorithms independently, multi-layer perceptron artificial neural network (MLP-ANN) and support vector regression (SVR) models to predict annual MSW generation rates in Bahrain. Models were trained and tested with MSW generation data for period of 1997-2019. The population, gross domestic product, annual tourist numbers, annual electricity consumption and total annual CO₂ emissions were selected as explanatory variables and incorporated into developed models. The zero score normalization (ZSN) and minimum maximum normalization (MMN) methods were utilized to improve the quality of data and subsequently enhances the performance of ML algorithms. Statistical metrics were employed to discriminate performance of MLP-ANN and SVR models. The linear, polynomial, radial basis function (RBF) and sigmoid kernel functions were investigated to find the optimal SVR model. Results showed that RBF-SVR model with R² value of 0.97% and 4.82% and absolute forecasting error (AFE) for the period of 2008 and 2019 exhibits superior prediction and robustness in comparison to MLP-ANN. The efficacy of MLP-ANN model was also reasonably successful with R² value of 0.94. It was shown that MMN pre-processing generated optimal MLP-ANN model while ZSN pre-processing produced optimal RBF-SVR model. This work also highlights the importance of application of ML modelling approaches to plan and implement their roadmap for waste management systems by policymakers.

Predictive modelling as a tool for effective municipal waste management policy at different territorial levels

Nowadays, the European municipal waste management policy based on the circular economy paradigm demands the closing of material and financial loops at all territorial levels of public administration. The effective planning of treatment capacities (especially sorting plants, recycling, and energy recovery facilities) and municipal waste management policy requires an accurate prognosis of municipal waste generation, and therefore, the knowledge of behavioral, socio-economic, and demographic factors influencing the waste management (and recycling) behavior of households, and other municipal waste producers. To enable public bodies at different territorial levels to undertake an effective action resulting in circular economy we evaluated various factors influencing the generation of municipal waste fractions at regional, micro-regional and municipal level in the Czech Republic. Principal components were used as input for traditional models (multivariable linear regression, generalized linear model) as well as tree-based machine learning models (regression trees, random forest, gradient boosted regression trees). Study results suggest that the linear regression model usually offers a good trade-off between model accuracy and interpretability. When the most important goal of the prediction is supposed to be accuracy, the random forest is generally the best choice. The quality of developed models depends mostly on the chosen territorial level and municipal waste fraction. The performance of these models deteriorates significantly for lower territorial levels because of worse data quality and bigger variability. Only the age structure seems to be important across territorial levels and municipal waste fractions. Nevertheless, also other factors are of high significance in explaining the generation of municipal waste fractions at different territorial levels (e.g. number of economic subjects, expenditures, population density and the level of education). Therefore, there is not one single effective public policy dealing with circular economy strategy that fits all territorial levels. Public representatives should focus on policies effective at specific territorial level. However, performance of the models is poor for lower territorial levels (municipality and micro-regions). Thus, results for municipalities and micro-regions are weak and should be treated as such.

Use of GIS and Moran's I to support residential solid waste recycling in the city of Annaba, Algeria

Urban planners require an understanding of the composition as well as the spatial distribution of household solid waste (HSW) components to design policies for various wards of a city. This paper aims to study the composition as well as the spatial dependency of the HSW components generated by the wards forming Annaba city in Algeria. The results of the HSW composition have revealed the high content of the organic matters which represents 50%, the textiles with 13.6%, and the plastics with 10% of the total quantity; the rest of the composition was 5.9% of paper and paperboard, 3% of metals, and 1% of glass. Furthermore, the result of the global and the local Moran indexes calculated and mapped through the ArcGIS 10.7 software shows that there is an evident spatial dependency for almost all the HSW components. Thus, 18% of the total quantity is produced by clustered wards, which propose the segregation of individual waste components at the source as an efficient way to support the reuse, recovery, and recycling. The greenhouse gas (GHG) emissions have revealed that 60% of the emissions are the methane produced from the landfill, and 30% is due to the open burning in the air of waste.

Use of GIS and Moran's I to support residential solid waste recycling in the city of Annaba, Algeria

Urban planners require an understanding of the composition as well as the spatial distribution of household solid waste (HSW) components to design policies for various wards of a city. This paper aims to study the composition as well as the spatial dependency of the HSW components generated by the wards forming Annaba city in Algeria. The results of the HSW composition have revealed the high content of the organic matters which represents 50%, the textiles with 13.6%, and the plastics with 10% of the total quantity; the rest of the composition was 5.9% of paper and paperboard, 3% of metals, and 1% of glass. Furthermore, the result of the global and the local Moran indexes calculated and mapped through the ArcGIS 10.7 software shows that there is an evident spatial dependency for almost all the HSW components. Thus, 18% of the total quantity is produced by clustered wards, which propose the segregation of individual waste components at the source as an efficient way to support the reuse, recovery, and recycling. The greenhouse gas (GHG) emissions have revealed that 60% of the emissions are the methane produced from the landfill, and 30% is due to the open burning in the air of waste.

Is there a waste Kuznets curve for OECD? Some evidence from panel analysis

We empirically investigated the existence of a Kuznets curve for OECD area in terms of municipal solid waste generation by utilizing a panel data covering the 2002-2017 period. Results from GMM and between estimators conclude that an inverted U-shaped Kuznets relationship holds for OECD countries, but only in relative terms. Results show that consumption expenditure is one of the main drivers of waste generation. Country-based estimations of random coefficient approach state that 8 out of 16 high-income countries and only 2 out of low-income countries have achieved decoupling the waste generation from consumption expenditures. Some policy implications are recommended.

Identification and Analysis of Sets Variables for of Municipal Waste Management Modelling

Due to the large quantities of municipal waste generated, their harmful effects on the environment should be minimized. The rationalization of waste management is therefore necessary to achieve a more sustainable development system. In order to support the decision-making process for municipal waste management, this document focuses on developing models for practical use by local authorities in forecasting and managing the size of waste stream in their area. This action, because of its specificity, is a difficult task, especially because of the systemic changes made and the territorial differentiation and changes in the living level of the population. The work presents studies conducted in 2479 municipalities for which mass accumulation index forecasts were developed, using selected methods based on readily available input variables that have not yet been used (structure municipalities and typology of municipalities by scope of influence). The studies confirmed the hypothesis that the amount of municipal waste collected from households depends both on the administrative type of the municipality and on the factors related to the location and socioeconomic function of the area. The inclusion of localization and socioeconomic factors, which so far were not used to model the municipal waste stream, allowed for the reduction of the prediction error of this indicator. Relevant waste stream forecasts will allow local governments to achieve more effectively the objective of sustainable waste management and thus reduce their environmental impact. The achievement of this objective will be possible not only through the preparation of infrastructure to serve the projected waste volumes; it will also identify the waste management areas where the municipal waste reception process is inadequate. Thus, it will help to eliminate illegal processing and the landfill of waste.

Hybrid model for the prediction of municipal solid waste generation in Hangzhou, China

Accurate prediction of municipal solid waste (MSW) generation is necessary for choosing appropriate waste treatment methods and for planning the distribution of disposal facilities. In this study, a hybrid model was established to forecast MSW generation through the combination of the ridge regression and GM(1,N) models. The hybrid model is multivariate and involves total urban population, total retail sales of social consumer goods, per capita consumption expenditure of urban areas, tourism, and college graduation. Compared with the constituent models alone, the hybrid model yields higher accuracy, with a mean absolute percentage error (MAPE) of only 2.59%. Through weight allocation and optimal treatment of residuals, the hybrid model also balances the growth trends of the individual models, making the prediction curve smoother. The model coefficients and correlation analysis show that population, economics, and educational factors are influential for waste generation. MSW output in Hangzhou will gradually increase in the future, and is expected to reach 5.12 million tons in 2021. Results can help decision makers to develop the measures and policies of waste management in the future.

Estimation of the generation rate of different types of plastic wastes and possible revenue recovery from informal recycling

Plastic waste generation is an inevitable product of human activities, however its management faces challenges in many cities. Understanding the existing patterns of plastic waste generation and recycling is essential for effective management planning. The present study established a relationship between plastic waste generation rate and the identified socioeconomic groups, higher socioeconomic group (HSEG), middle socioeconomic group (MSEG), and lower socioeconomic group (LSEG) of the study area (Dhanbad, India). For identification of the socioeconomic groups, four different socioeconomic parameters were considered (total family income, education, occupation and type of houses). The information related to the identified parameters were obtained using questionnaire survey conducted in the selected households. One week plastic waste sampling was carried out in the households of all the socioeconomic groups. The plastic waste generated in the study area was 5.7% of the total municipal solid waste. In terms of total plastic waste generation rate, it was found that HSEG had maximum (51 g/c/d) and LSEG had minimum (8 g/c/d) generation rate. The present study area does not have any formal waste recycling system. Thus, the amount of plastic waste recovered and the revenue generated from recycling of plastic waste by the active informal recyclers (waste pickers, itinerant waste buyers and scrap dealers) in the study area have been evaluated. Additionally, three non-linear machine learning models i.e., artificial neural network (ANN), support vector machine (SVM) and random forest (RF) have been developed and compared for the prediction of plastic waste generation rate.

Estimating solid waste generation by hospitality industry during major festivals: A quantification model based on multiple regression

Major-religious festivals hosted in the city of Kerbala, Iraq, annually generate large quantities of Municipal Solid Waste (MSW) which negatively impacts the environment and human health when poorly managed. The hospitality sector, specifically hotels, is one of the major sources of MSW generated during these festivals. Because it is essential to establish a proper waste management system for such festivals, accurate information regarding MSW generation is required. This study therefore investigated the rate of production of MSW from hotels in Kerbala during major festivals. A field questionnaire survey was conducted with 150 hotels during the Arba'een festival, one of the largest festivals in the world, attended by about 18 million participants, to identify how much MSW is produced and what features of hotels impact on this. Hotel managers responded to questions regarding features of the hotel such as size (Hs), expenditure (Hex), area (Ha) and number of staff (Hst). An on-site audit was also carried out with all participating hotels to estimate the mass of MSW generated from these hotels. The results indicate that MSW produced by hotels varies widely. In general, it was found that each hotel guest produces an estimated 0.89 kg of MSW per day. However, this figure varies according to the hotels' rating. Average rates of MSW production from one and four star hotels were 0.83 and 1.22 kg per guest per day, respectively. Statistically, it was found that the relationship between MSW production and hotel features can be modelled with an R² of 0.799, where the influence of hotel feature on MSW production followed the order Hs > Hex > Hst.

Analysis of the waste selective collection at drop-off systems: Case study including the income level and the seasonal variation

There are several factors which have an influence in the selective collection of the municipal waste. To define a selective collection system, the waste generation pattern should be firstly determined and these factors should be analyzed in depth. This paper tries to analyze the economic income level and the seasonal variation on the collection and the purity of light-packaging waste to determine actions to improve the waste management plan of a town. In the first stage of the work, waste samples of the light-packaging containers were collected in two zones of the town with different economic characteristics in different seasons during one year. In the second stage, the samples were characterized to analyze the composition and purity of the waste. They were firstly separated into four fractions: metals; plastic; beverage cartons; and misplaced materials. The misplaced fraction was in its turn separated into cardboard, rubber and leather, inert waste, organic matter, paper, hazardous waste, clothes and shoes, glass and others. The plastic fraction was separated into five types of plastics and the metal fraction into three. In the third stage, the data have been analyzed and conclusions have been extracted. The main result is that the quality of the light-packaging fraction collected in these zones during both seasons were similar. This methodology can be extrapolated to towns with similar characteristics. It will be useful when implementing a system to collect the waste selectively and to develop actions to achieve a good participation in the selective collection of the waste.

An empirical model for prediction of household solid waste generation rate - A case study of Dhanbad, India

Accurate prediction of the quantity of household solid waste generation is very much essential for effective management of municipal solid waste (MSW). In actual practice, modelling methods are often found useful for precise prediction of MSW generation rate. In this study, two models have been proposed that established the relationships between the household solid waste generation rate and the socioeconomic parameters, such as household size, total family income, education, occupation and fuel used in the kitchen. Multiple linear regression technique was applied to develop the two models, one for the prediction of biodegradable MSW generation rate and the other for non-biodegradable MSW generation rate for individual households of the city Dhanbad, India. The results of the two models showed that the coefficient of determinations (R²) were 0.782 for biodegradable waste generation rate and 0.676 for non-biodegradable waste generation rate using the selected independent variables. The accuracy tests of the developed models showed convincing results, as the predicted values were very close to the observed values. Validation of the developed models with a new set of data indicated a good fit for actual prediction purpose with predicted R² values of 0.76 and 0.64 for biodegradable and non-biodegradable MSW generation rate respectively.

Generation and collection of restaurant waste: Characterization and evaluation at a case study in Italy

Because restaurants (as a division of the hospitality sector) contribute to the generation of commercial and institutional waste, thus representing both a challenge and an opportunity, the objective of the present study was to deepen the knowledge of restaurant waste in terms of the qualitative and quantitative characteristics of waste generation and the performance achievable by the implementation of a separate collection scheme. In this study, the generated waste was characterized and the implemented separate collection was evaluated at a relevant case study restaurant in a coastal tourist area of Central Italy (Marche Region, Adriatic Sea side). The qualitative (compositional) characterization of the generated total restaurant waste showed considerable incidences of, in decreasing order, food (28.2%), glass (22.6%), paper/cardboard (19.1%), and plastic (17.1%). The quantitative (parametric) characterization of the generated restaurant waste determined the unit generation values of total waste and individual fractions based on the traditional employee and area parameters and the peculiar meal parameter. In particular, the obtained representative values per meal were: 0.72kgmeal^-1 for total waste, and ranging, for individual fractions, from 0.20 (for food) to 0.008kgmeal^-1 (for textile). Based on the critical evaluation of some of the resulting unit waste generation values, possible influences of restaurant practices, conditions, or characteristics were pointed out. In particular, food waste generation per meal can likely be limited by: promoting and using local, fresh, and quality food; standardizing and limiting daily menu items; basing food recipes on consolidated cooking knowledge and experience; and limiting plate sizes. The evaluation of the monthly variation of the monitored separate collection, ranging from an higher level of 52.7% to a lower level of 41.4%, indicated the following: a reduction in the separate collection level can be expected at times of high working pressure or the closing of a seasonal business (typical for restaurants in tourist areas); and the monthly variation of the separate collection level is inversely correlated with that of the unit generation of total waste per meal. The interception rates of the different restaurant waste fractions collected separately presented a ranking order (i.e., 96.0% for glass, 67.7% for paper/cardboard, 34.4% for food, 20.6% for metal, and 17.9% for plastic) similar to the order of efficiencies achievable at both small and large urban levels. Finally, the original concept of the customer equivalent person (P_ce) was introduced and behaviorally evaluated at the case study restaurant, providing the values of 0.42 and 0.39kgP_ce^-1day^-1 for the food waste generation and the landfilling of biodegradable waste by the customer equivalent person, respectively. These values were compared, respectively, with the food waste generation per person at the household level and the landfilling of biodegradable waste per inhabitant at the territorial level.

The role of awareness campaigns in the improvement of separate collection rates of municipal waste among university students: A Causal Chain Approach

One of the main objectives of municipal waste management policies is to improve separate collection, both quantitatively and qualitatively. Several factors influence people behavior to recycling and, consequently, they play an important role to achieve the goals proposed in the management policies. People can improve separate collection rates because of a wide range of causes with different weight. Here, we have determined the uplift in probability to improve separate collection of municipal waste created by the awareness campaigns among 806 undergraduate students at Universitat Rovira i Virgili (Catalonia) by means of the Causal Chain Approach, a probabilistic method. A 73.2% state having improved separate collection in recent years and the most of them (75.4%) remember some awareness campaign. The results show the uplift in probability to improve separate collection attributable to the awareness campaigns is 17.9%. They should be taken into account by policy makers in charge of municipal waste management. Nevertheless, it must be assumed an awareness campaign will never be sufficient to achieve the objectives defined in municipal waste management programmes.

Assessing the variables affecting on the rate of solid waste generation and recycling: An empirical analysis in Prespa Park

Economic development, urbanization, and improved living standards increase the quantity and complexity of generated solid waste. Comprehensive study of the variables influencing household solid waste production and recycling rate is crucial and fundamental for exploring the generation mechanism and forecasting future dynamics of household solid waste. The present study is employed in the case study of Prespa Park. A model, based on the interrelationships of economic, demographic, housing structure and waste management policy variables influencing the rate of solid waste generation and recycling is developed and employed. The empirical analysis is based on the information derived from a field questionnaire survey conducted in Prespa Park villages for the year 2014. Another feature of this study is to test whether a household's waste generation can be decoupled from its population growth. Descriptive statistics, bivariate correlation analysis and F-tests are used to know the relationship between variables. One-way and two-way fixed effects models data analysis techniques are used to identify variables that determine the effectiveness of waste generation and recycling at household level in the study area. The results reveal that households with heterogeneous characteristics, such as education level, mean building age and income, present different challenges of waste reduction goals. Numerically, an increase of 1% in education level of population corresponds to a waste reduction of 3kg on the annual per capita basis. A village with older buildings, in the case of one year older of the median building age, corresponds to a waste generation increase of 12kg. Other economic and policy incentives such as the mean household income, pay-as-you-throw, percentage of population with access to curbside recycling, the number of drop-off recycling facilities available per 1000 persons and cumulative expenditures on recycling education per capita are also found to be effective measures in waste reduction. The mean expenditure for recycling education spent on a person for years 2010 and 2014 is 12 and 14 cents, respectively and it vary from 0 to €1. For years 2010 and 2014, the mean percentage of population with access to curbside recycling services is 38.6% and 40.3%, and the mean number of drop-off recycling centers per 1000 persons in the population is 0.29 and 0.32, respectively. Empirical evidence suggests that population growth did not necessarily result in increases in waste generation. The results provided are useful when planning, changing or implementing sustainable municipal solid waste management.

Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China

The quantities and composition of municipal solid waste (MSW) are important factors in the planning and management of MSW. Daily human activities were classified into three groups: maintenance activities (meeting the basic needs of food, housing and personal care, MA); subsistence activities (providing the financial support requirements, SA); and leisure activities (social and recreational pursuits, LA). A model, based on the interrelationships of expenditure on consumer goods, time distribution, daily activities, residents groups, and waste generation, was employed to estimate MSW generation by different activities and resident groups in five provinces (Zhejiang, Guangdong, Hebei, Henan and Sichuan) of China. These five provinces were chosen for this study and the distribution patterns of MSW generated by different activities and resident groups were revealed. The results show that waste generation in SA and LA fluctuated slightly from 2003 to 2008. For general waste generation in the five provinces, MA accounts for more than 70% of total MSW, SA approximately 10%, and LA between 10% and 16% by urban residents in 2008. Females produced more daily MSW than males in MA. Males produced more daily MSW than females in SA and LA. The wastes produced at weekends in MA and LA were far greater than on weekdays, but less than on weekdays for SA wastes. Furthermore, one of the model parameters (the waste generation per unit of consumer expenditure) is inversely proportional to per-capita disposable income of urban residents. A significant correlation between gross domestic product (GDP) and waste generation by SA was observed with a high coefficient of determination.

Forecasting of municipal solid waste quantity in a developing country using multivariate grey models

In order to plan, manage and use municipal solid waste (MSW) in a sustainable way, accurate forecasting of MSW generation and composition plays a key role. It is difficult to carry out the reliable estimates using the existing models due to the limited data available in the developing countries. This study aims to forecast MSW collected in Thailand with prediction interval in long term period by using the optimized multivariate grey model which is the mathematical approach. For multivariate models, the representative factors of residential and commercial sectors affecting waste collected are identified, classified and quantified based on statistics and mathematics of grey system theory. Results show that GMC (1, 5), the grey model with convolution integral, is the most accurate with the least error of 1.16% MAPE. MSW collected would increase 1.40% per year from 43,435-44,994 tonnes per day in 2013 to 55,177-56,735 tonnes per day in 2030. This model also illustrates that population density is the most important factor affecting MSW collected, followed by urbanization, proportion employment and household size, respectively. These mean that the representative factors of commercial sector may affect more MSW collected than that of residential sector. Results can help decision makers to develop the measures and policies of waste management in long term period.

Identification of influencing municipal characteristics regarding household waste generation and their forecasting ability in Biscay

The planning of waste management strategies needs tools to support decisions at all stages of the process. Accurate quantification of the waste to be generated is essential for both the daily management (short-term) and proper design of facilities (long-term). Designing without rigorous knowledge may have serious economic and environmental consequences. The present works aims at identifying relevant socio-economic features of municipalities regarding Household Waste (HW) generation by means of factor models. Factor models face two main drawbacks, data collection and identifying relevant explanatory variables within a heterogeneous group. Grouping similar characteristics observations within a group may favour the deduction of more robust models. The methodology followed has been tested with Biscay Province because it stands out for having very different municipalities ranging from very rural to urban ones. Two main models are developed, one for the overall province and a second one after clustering the municipalities. The results prove that relating municipalities with specific characteristics, improves the results in a very heterogeneous situation. The methodology has identified urban morphology, tourism activity, level of education and economic situation as the most influencing characteristics in HW generation.

Household waste compositional analysis variation from insular communities in the framework of waste prevention strategy plans

Waste management planning requires reliable data regarding waste generation, affecting factors on waste generation and forecasts of waste quantities based on facts. In order to decrease the environmental impacts of waste management the choice of prevention plan as well as the treatment method must be based on the features of the waste that are produced in a specific area. Factors such as culture, economic development, climate, and energy sources have an impact on waste composition; composition influences the need of collecting waste more or less frequently of waste collection and disposition. The research question was to discover the main barriers concerning the compositional analysis in Insular Communities under warm climate conditions and the findings from this study enabled the main contents of a waste management plan to be established. These included advice to residents on waste minimisation, liaison with stakeholders and the expansion of kerbside recycling schemes.

Scenario analysis of the benefit of municipal organic-waste composting over landfill, Cambodia

This paper presents insight into the benefits of organic waste recycling through composting over landfill, in terms of landfill life extension, compost product, and mitigation of greenhouse gases (GHGs). Future waste generation from 2003 to 2020 was forecast, and five scenarios of organic waste recycling in the municipality of Phnom Penh (MPP), Cambodia, were carried out. Organic waste-specifically food and garden waste-was used for composting, and the remaining waste was landfilled. The recycling scenarios were set based on organic waste generated from difference sources: households, restaurants, shops, markets, schools, hotels, offices, and street sweeping. Through the five scenarios, the minimum volume reductions of waste disposal were about 56, 123, and 219 m(3) d(-1) in 2003, 2012, and 2020, respectively, whereas the maximum volume reductions in these years were about 325, 643, and 1025 m(3) d(-1). These volume reductions reflect a landfill life extension of a minimum of half a year and a maximum of about four years. Compost product could be produced at a minimum of 14, 30, and 54 tons d(-1) in 2003, 2012, and 2020, respectively, and at a maximum in those years of about 80, 158, and 252 tons d(-1). At the same time benefit is gained in compost product, GHG emissions could be reduced by a minimum of 12.8% and a maximum of 65.0% from 2003 to 2020. This means about 3.23 (minimum) and 5.79 million tons CO(2)eq (maximum) contributed to GHG mitigation. In this regard, it is strongly recommended that MPP should try to initiate an organic-waste recycling strategy in a best fit scenario.

Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey

In studies focusing on the factors that impact solid waste generation habits and rates, the potential spatial dependency in solid waste generation data is not considered in relating the waste generation rates to its determinants. In this study, spatial dependency is taken into account in determination of the significant socio-economic and climatic factors that may be of importance for the municipal solid waste (MSW) generation rates in different provinces of Turkey. Simultaneous spatial autoregression (SAR) and geographically weighted regression (GWR) models are used for the spatial data analyses. Similar to ordinary least squares regression (OLSR), regression coefficients are global in SAR model. In other words, the effect of a given independent variable on a dependent variable is valid for the whole country. Unlike OLSR or SAR, GWR reveals the local impact of a given factor (or independent variable) on the waste generation rates of different provinces. Results show that provinces within closer neighborhoods have similar MSW generation rates. On the other hand, this spatial autocorrelation is not very high for the exploratory variables considered in the study. OLSR and SAR models have similar regression coefficients. GWR is useful to indicate the local determinants of MSW generation rates. GWR model can be utilized to plan waste management activities at local scale including waste minimization, collection, treatment, and disposal. At global scale, the MSW generation rates in Turkey are significantly related to unemployment rate and asphalt-paved roads ratio. Yet, significances of these variables may diminish at local scale for some provinces. At local scale, different factors may be important in affecting MSW generation rates.

Estimating municipal solid waste generation by different activities and various resident groups: a case study of Beijing

Reliable and accurate determinations of the quantities and composition of wastes is required for the planning of municipal solid waste (MSW) management systems. A model, based on the interrelationships of expenditure on consumer goods, time distribution, daily activities, residents groups, and waste generation, was developed and employed to estimate MSW generation by different activities and resident groups in Beijing. The principle is that MSW is produced by consumption of consumer goods by residents in their daily activities: 'Maintenance' (meeting the basic needs of food, housing and personal care), 'Subsistence' (providing the financial requirements) and 'Leisure' (social and recreational pursuits) activities. Three series of important parameters - waste generation per unit of consumer expenditure, consumer expenditure distribution to activities in unit time, and time assignment to activities by different resident groups - were determined using a statistical analysis, a sampling survey and the Analytic Hierarchy Process, respectively. Data for analysis were obtained from the Beijing Statistical Yearbook (2004-2008) and questionnaire survey. The results reveal that 'Maintenance' activity produced the most MSW, distantly followed by 'Leisure' and 'Subsistence' activities. In 2008, in descending order of MSW generation the different resident groups were floating population, non-civil servants, retired people, civil servants, college students (including both undergraduates and graduates), primary and secondary students, and preschoolers. The new estimation model, which was successful in fitting waste generation by different activities and resident groups over the investigated years, was amenable to MSW prediction.

Targeted intervention strategies to optimise diversion of BMW in the Dublin, Ireland region

Urgent transformation is required in Ireland to divert biodegradable municipal waste (BMW) from landfill and prevent increases in overall waste generation. When BMW is optimally managed, it becomes a resource with value instead of an unwanted by-product requiring disposal. An analysis of survey responses from commercial and residential sectors for the Dublin region in previous research by the authors proved that attitudes towards and behaviour regarding municipal solid waste is spatially variable. This finding indicates that targeted intervention strategies designed for specific geographic areas should lead to improved diversion rates of BMW from landfill, a requirement of the Landfill Directive 1999/31/EC. In the research described in this paper, survey responses and GIS model predictions from previous research were the basis for goal setting, after which logic modelling and behavioural research were employed to develop site-specific waste management intervention strategies. The main strategies devised include (a) roll out of the Brown Bin (Organics) Collection and Community Workshops in Dún Laoghaire Rathdown, (b) initiation of a Community Composting Project in Dublin City (c) implementation of a Waste Promotion and Motivation Scheme in South Dublin (d) development and distribution of a Waste Booklet to promote waste reduction activities in Fingal (e) region wide distribution of a Waste Booklet to the commercial sector and (f) Greening Irish Pubs Initiative. Each of these strategies was devised after interviews with both the residential and commercial sectors to help make optimal waste management the norm for both sectors. Strategy (b), (e) and (f) are detailed in this paper. By integrating a human element into accepted waste management approaches, these strategies will make optimal waste behaviour easier to achieve. Ultimately this will help divert waste from landfill and improve waste management practice as a whole for the region. This method of devising targeted intervention strategies can be adapted for many other regions.

Municipal solid waste generation in municipalities: quantifying impacts of household structure, commercial waste and domestic fuel

Waste management planning requires reliable data concerning waste generation, influencing factors on waste generation and forecasts of waste quantities based on facts. This paper aims at identifying and quantifying differences between different municipalities' municipal solid waste (MSW) collection quantities based on data from waste management and on socio-economic indicators. A large set of 116 indicators from 542 municipalities in the Province of Styria was investigated. The resulting regression model included municipal tax revenue per capita, household size and the percentage of buildings with solid fuel heating systems. The model explains 74.3% of the MSW variation and the model assumptions are met. Other factors such as tourism, home composting or age distribution of the population did not significantly improve the model. According to the model, 21% of MSW collected in Styria was commercial waste and 18% of the generated MSW was burned in domestic heating systems. While the percentage of commercial waste is consistent with literature data, practically no literature data are available for the quantity of MSW burned, which seems to be overestimated by the model. The resulting regression model was used as basis for a waste prognosis model (Beigl and Lebersorger, in preparation).

Solid waste management in European countries: a review of systems analysis techniques

In the past few decades, solid waste management systems in Europe have involved complex and multi-faceted trade-offs among a plethora of technological alternatives, economic instruments, and regulatory frameworks. These changes resulted in various environmental, economic, social, and regulatory impacts in waste management practices which not only complicate regional policy analysis, but also reshape the paradigm of global sustainable development. Systems analysis, a discipline that harmonizes these integrated solid waste management strategies, has been uniquely providing interdisciplinary support for decision making in this area. Systems engineering models and system assessment tools, both of which enrich the analytical framework of waste management, were designed specifically to handle particular types of problems. Though how to smooth out the barriers toward achieving appropriate systems synthesis and integration of these models and tools to aid in the solid waste management schemes prevalent in European countries still remains somewhat uncertain. This paper conducts a thorough literature review of models and tools illuminating possible overlapped boundaries in waste management practices in European countries and encompassing the pros and cons of waste management practices in each member state of the European Union. Whereas the Southern European Union (EU) countries need to develop further measures to implement more integrated solid waste management and reach EU directives, the Central EU countries need models and tools with which to rationalize their technological choices and management strategies. Nevertheless, considering systems analysis models and tools in a synergistic way would certainly provide opportunities to develop better solid waste management strategies leading to conformity with current standards and foster future perspectives for both the waste management industry and government agencies in European Union.

Attitudes and behaviour towards waste management in the Dublin, Ireland region

The hypothesis of this research was that attitudes about the management of biodegradable municipal waste (BMW) are spatially variable, even within a city of modest (1.2 million) population. For a select number of representative electoral districts in the Dublin, Ireland region, residents were surveyed regarding attitudes towards waste management in general, and BMW management in particular. A total of 850 survey responses were collected. Door-to-door interviews produced 688 responses in the residential sector; these were supplemented by 162 responses to a web-based survey. The surveys revealed that the majority of households use local authority, rather than private, waste collection services (both are available). The majority of residents, regardless of the local authority in which they live, were satisfied with their waste management service. "Reducing the quantity of waste generated" was regarded the most important future issue for 28% of residential respondents. Statistical analyses of the survey responses showed that the local authority in which respondents resided significantly influenced most responses (including waste collection service used, waste service satisfaction and backyard composting activity). Many responses (including waste service satisfaction, waste management influences) were also significantly related to the respondents' personal characteristics (e.g., education level, type of accommodation, age, etc.). These statistical results proved the hypothesis of the research and demonstrated that waste management initiatives designed for one area of the city (or, indeed, for uniform application to the city as a whole) could ignore the needs of other areas. The survey responses suggest that targeted intervention strategies would lead to improved diversion rates of BMW from landfill, a requirement of the Landfill Directive 1999/31/EC.