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The introduction of mobile telecommunication services in Nigeria led to the development of base transceiver stations (BTS) across the country. Inadequate power supply from the national grid has led to massive use of diesel-fueled back-up generators (BUGs). The purpose of this paper is to attempt to quantify and inform relevant stakeholders about air quality implications of BTS BUGs.

Seven major telecommunication network operators were identified. Emission factor approach was used to estimate the quantity of important air pollutants such as NO_x, CO, SO₂, PM₁₀, PM_2.5, PAH and TVOC that are emitted from the use of the BUGs based on fuel consumption rate and generators’ capacity. Fuel-based emission inventory and emission factor from the United States Environmental Protection Agency AP-42 and National Pollution Inventory were used to estimate pollutants emission from diesel-powered generators used in the BTS sites and amount of diesel consumed. Land distribution and per capita dose of the estimated pollutants load were calculated.

The study showed that the deployment of BUGs will lead to increase emissions of these air pollutants. The states that are most affected are Lagos, Kano and Oyo, Katsina and Akwa Ibom states with respective total air pollutants contribution of 9,539.61, 9,445.34, 8,276.46, 7,805.14 and 7,220.70 tonnes/yr.

This study has estimated pollutant emissions from the use of diesel-fueled BUGs in mobile telecommunications BTS sites in Nigeria. The data obtained could assist in policy making.

The purpose of this paper is to compute the amount of methane generated from the waste of livestock from 1980 to 2008; then use the information in forecasting subsequent methane emissions by the sector in Malaysia from 2009 to 2020.

The research study employed two approaches; computing methane emissions from 1980 to 2008 using the IPCC guidelines, and forecasting methane emissions for the animals from 2009 to 2020 using the autoregressive integrated moving average (ARIMA) model from the predictive analysis software (PASW-SPSS 18.0).

Methane emissions from cattle, buffaloes and pigs accounted for over 95 per cent of total emissions and emissions from cattle are predicted to increase from 67.0 Gg in 2009 to 77.0 Gg by 2020. Emissions from the others will not be appreciable although poultry emissions will rise to 11.0 Gg by 2020. Attempt by the Malaysian Government to increase cattle production is not necessary at the moment as protein requirement has been met.

ARIMA model suffers from linear and data limitation: the future value of a variable assumed to be a linear function of several past observations in ARIMA is sometimes unrealistic. Large amounts of historical data are needed in ARIMA models in order to get desired results. The inventory of the animals was taken from 1980 to 2008.This needs to be improved upon by updating it to cover up to 2011 so that the forecast will start from 2012.

The chosen ARIMA method has demonstrated its correctness in being adequate as a predicting tool for animal methane emissions. Policy makers can apply it so as to take practical steps to avoid these emissions.

This is a novice idea as animal methane emission forecasting tool. This model will be of immense use and help in predicting methane emissions from livestock.

This study aims to investigate the macroeconomic factors influencing housing demand in Saudi Arabia using quarterly data for the period 2010 Q1–2024 Q2.

The autoregressive distributed lag bounds test and vector error correction model are used to determine the dynamic relationship between the macroeconomic factors.

The results reveal that the F-statistic value of 4.2043 is higher than the upper bound of the critical values at 5% level of significance, indicating long-run association among the series. While there is a negative and significant influence of interest and inflation rates on housing demand, income per capita and population have a positive and significant influence on housing demand. Similar result is found for the short run analysis except income per capita which has positive but insignificant impact. Moreso, the population seems to be very crucial to housing demand in both short-run and long-run periods.

The outcome of the study has important policy implications by enjoining the policymakers in Saudi Arabia to consider a decisive management of monetary and fiscal policies to ensure that inflation and interest rates for housing are kept at a low level to encourage housing demand. Also, adequate policies should be put in place to ensure housing/property affordability that matches the increasing population due to tourism, urbanisation and growing birth rate.

Studies on the macroeconomic determinants of housing demand are less explored as most studies dwell more on social and demographic factors despite the importance of macroeconomic factors towards house ownerships. There is a dearth of related studies in Saudi Arabia.