scholarly journals Determining the sensitivity of grassland area burned to climate variation in Xilingol, China, with an autoregressive distributed lag approach

2019 ◽  
Vol 28 (8) ◽  
pp. 628 ◽  
Author(s):  
Ali Hassan Shabbir ◽  
Jiquan Zhang ◽  
Xingpeng Liu ◽  
James A. Lutz ◽  
Carlos Valencia ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wildland Fire ◽  
Strong Relationship ◽  
Maximum Temperature ◽  
Climate Variables ◽  
Area Burned ◽  
Distributed Lag ◽  
Temperature And Precipitation ◽  
Autoregressive Distributed Lag ◽  
The Relationship

We examined the relationship between climate variables and grassland area burned in Xilingol, China, from 2001 to 2014 using an autoregressive distributed lag (ARDL) model, and describe the application of this econometric method to studies of climate influences on wildland fire. We show that there is a stationary linear combination of non-stationary climate time series (cointegration) that can be used to reliably estimate the influence of different climate signals on area burned. Our model shows a strong relationship between maximum temperature and grassland area burned. Mean monthly wind speed and monthly hours of sunlight were also strongly associated with area burned, whereas minimum temperature and precipitation were not. Some climate variables like wind speed had significant immediate effects on area burned, the strength of which varied over the 2001–14 observation period (in econometrics terms, a ‘short-run’ effect). The relationship between temperature and area burned exhibited a steady-state or ‘long-run’ relationship. We analysed three different periods (2001–05, 2006–10 and 2011–14) to illustrate how the effects of climate on area burned vary over time. These results should be helpful in estimating the potential impact of changing climate on the eastern Eurasian Steppe.

scholarly journals Predicting the influence of climate on grassland area burned in Xilingol, China with dynamic simulations of autoregressive distributed lag models

PLoS ONE ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. e0229894 ◽  
Author(s):  
Ali Hassan Shabbir ◽  
Jiquan Zhang ◽  
James D. Johnston ◽  
Samuel Asumadu Sarkodie ◽  
James A. Lutz ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Inner Mongolia ◽  
Northern China ◽  
Maximum Temperature ◽  
Science Literature ◽  
Dynamic Simulations ◽  
Area Burned ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag

The influence of climate change on wildland fire has received considerable attention, but few studies have examined the potential effects of climate variability on grassland area burned within the extensive steppe land of Eurasia. We used a novel statistical approach borrowed from the social science literature—dynamic simulations of autoregressive distributed lag (ARDL) models—to explore the relationship between temperature, relative humidity, precipitation, wind speed, sunlight, and carbon emissions on grassland area burned in Xilingol, a large grassland-dominated landscape of Inner Mongolia in northern China. We used an ARDL model to describe the influence of these variables on observed area burned between 2001 and 2018 and used dynamic simulations of the model to project the influence of climate on area burned over the next twenty years. Our analysis demonstrates that area burned was most sensitive to wind speed and temperature. A 1% increase in wind speed was associated with a 20.8% and 22.8% increase in observed and predicted area burned respectively, while a 1% increase in maximum temperature was associated with an 8.7% and 9.7% increase in observed and predicted future area burned. Dynamic simulations of ARDL models provide insights into the variability of area burned across Inner Mongolia grasslands in the context of anthropogenic climate change.

scholarly journals Nexus between Climate Change and Agricultural Production in Odisha, India: An ARDL Approach

2021 ◽  
Vol 6 (2) ◽  
pp. 136-144
Author(s):  
Pratap Kumar Jena
Keyword(s):  
Climate Change ◽  
Agricultural Production ◽  
Agricultural Research ◽  
Climate Variables ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Ardl Approach ◽  
The Government ◽  
The Impact ◽  
The Relationship

Climate change is an emerging issue particularly in agricultural research as it is observed that the climate change has unfavorably distressed the agricultural production in different regions in India. Therefore, the present study has empirically examined the relationship between climate change and agricultural production in the selected districts of Odisha, India using a Panel Autoregressive Distributed Lag (PARDL) model over the period 1993 to 2019. The study found that the climate variables have adversely affected the crops production in the districts of Odisha. In order to minimize the impact of climate change on crops production in the state, there must have implementation of various policies and adaptive strategies by the government and farmers.

scholarly journals Relationship between Climate Variables and New Daily COVID-19 Cases in Dhaka, Bangladesh

2020 ◽  
Author(s):  
M. Mofijur ◽  
Islam Md Rizwanul Fattah ◽  
A. B. M. Saiful Islam ◽  
S.M. Ashrafur Rahman ◽  
Mohammad Asaduzzaman Chowdhury
Keyword(s):  
Air Quality ◽  
Wind Speed ◽  
Minimum Temperature ◽  
Maximum Temperature ◽  
Dhaka City ◽  
Climate Variables ◽  
Average Temperature ◽  
Climate Indicators ◽  
The World ◽  
The Relationship

The present study investigates the relationship between the transmission of COVID-19 infections and climate indicators in Dhaka City, Bangladesh, using coronavirus infections data available from the Institute of Epidemiology, Disease Control and Research (IEDCR), Bangladesh. The Spearman-ranked correlation test was carried out to study the association of seven climate indicators, including humidity, air quality, minimum temperature, precipitation, maximum temperature, mean temperature and wind speed with the COVID-19 outbreak in Dhaka City, Bangladesh. The study found that, among the seven indicators, only three indicators (air quality, minimum temperature and average temperature) have a significant relationship with new COVID-19 cases. The results of this paper will give health regulators and policymakers valuable information to lessen the COVID-19 infection in Dhaka and other countries around the world.

Impact of Environmental Tax on Comparative Advantage of Food and Food Products: A Study of G20 Countries in Light of Environmentally Sensitive Goods

2021 ◽  
pp. 001946622110352
Author(s):  
Alisha Mahajan ◽  
Kakali Majumdar
Keyword(s):  
Comparative Advantage ◽  
Food Products ◽  
Environmental Taxes ◽  
Environmental Tax ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Long Run ◽  
Short Run ◽  
Environmentally Sensitive ◽  
The Relationship

Many countries are under constant fear that environmental policies might negatively influence the international competitiveness of polluting industries. In this study, we aim to evaluate the relationship and impact of the environmental tax on comparative advantage of trade in food and food products industry, considered to be one of the highly environmentally sensitive industries. This study also investigates, whether this relationship differs among countries covered in G20, with the help of correlation analysis. We select panel autoregressive distributed lag approach for this study as it can analyse long-run as well as short-run association even when the variables are stationary at different orders of integration. Using panel data from G20 countries over the period of 21 years that is from 1994 to 2015, it is concluded that when we allow environmental taxes to interact with the revealed comparative advantage (RCA) of G20 nations, the overall impact of the environmental tax on the RCA is negative in the long period. It is therefore suggested that countries should follow Porter hypothesis to stimulate innovations resulting from strict environmental regulations that affect the environment in least possible manner. JEL Codes: C01, C23, C33, F18, O57, Q5

scholarly journals Specimen-based analysis of morphology and the environment in ecologically dominant grasses: the power of the herbarium

2018 ◽  
Vol 374 (1763) ◽  
pp. 20170403 ◽  
Author(s):  
Christine A. McAllister ◽  
Michael R. McKain ◽  
Mao Li ◽  
Bess Bookout ◽  
Elizabeth A. Kellogg
Keyword(s):  
Morphological Characters ◽  
Automated Image Analysis ◽  
Climate Variables ◽  
Theme Issue ◽  
Analysis Techniques ◽  
Temperature And Precipitation ◽  
Sessile Spikelet ◽  
Effects Of Temperature ◽  
The Relationship ◽  
Image Analysis Techniques

Herbaria contain a cumulative sample of the world's flora, assembled by thousands of people over centuries. To capitalize on this resource, we conducted a specimen-based analysis of a major clade in the grass tribe Andropogoneae, including the dominant species of the world's grasslands in the genera Andropogon , Schizachyrium , Hyparrhenia and several others. We imaged 186 of the 250 named species of the clade, georeferenced the specimens and extracted climatic variables for each. Using semi- and fully automated image analysis techniques, we extracted spikelet morphological characters and correlated these with environmental variables. We generated chloroplast genome sequences to correct for phylogenetic covariance and here present a new phylogeny for 81 of the species. We confirm and extend earlier studies to show that Andropogon and Schizachyrium are not monophyletic. In addition, we find all morphological and ecological characters are homoplasious but variable among clades. For example, sessile spikelet length is positively correlated with awn length when all accessions are considered, but when separated by clade, the relationship is positive for three sub-clades and negative for three others. Climate variables showed no correlation with morphological variation in the spikelet pair; only very weak effects of temperature and precipitation were detected on macrohair density. This article is part of the theme issue ‘Biological collections for understanding biodiversity in the Anthropocene'.

scholarly journals The link between urbanization and air pollution in Turkey: Evidence from dynamic Autoregressive Distributed Lag simulations

2021 ◽  
Author(s):  
Alper Aslan ◽  
BUKET ALTINOZ ◽  
BAKİ OZSOLAK
Keyword(s):  
Air Pollution ◽  
Energy Consumption ◽  
Co2 Emissions ◽  
Sustainable Growth ◽  
Similar Relationship ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Positive Shock ◽  
The Relationship

Abstract This study investigates the relationship between urbanization and air pollution in Turkey. Dynamic ARDL method was used for the period 1960–2014. According to the findings, there is a positive and statistically significant relationship between long-term urbanization and Co2. If urbanization increased by 1%, carbon emissions increased by 0.02%. There is a similar relationship between the shocks that will occur in population growth and Co2 emission in the long term. However, there is a negative and statistically insignificant relationship between the two variables. In the relationship between GDP and Co2, there is a positive relationship in the long term. GDP increase of 1% increases Co2 emissions by 0.11%. There is a similar relationship between long-term GDP shocks and Co2 emissions. According to short-term analysis results, energy consumption increases Co2 emissions by the same rate as GDP. However, the astonishing result of the study emerges here. Empirical results show that a long-term positive shock in energy consumption reduces CO2 emissions and a negative shock increases pollution. According to these results, Turkey has not reached the point of sustainable growth. For this reason, this developing country needs to make regulatory implementations and determine future policies for these impacts affecting air pollution.

scholarly journals The relationship between renewable energy and sustainable development in Indonesia

2021 ◽  
Vol 922 (1) ◽  
pp. 012034
Author(s):  
G Syamni ◽  
Wardhiah ◽  
Zulkifli ◽  
M J A Siregar ◽  
Y A Sitepu
Keyword(s):  
Economic Growth ◽  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Secondary Data ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Short And Long Term ◽  
The Relationship ◽  
Positive Effect

Abstract This paper is conducted to examine the relationship between the use of renewable energy and FDI in Indonesia. The data used in this study is secondary data that has been published by the World Bank and accessed in www.Data.worldbank.org. periode 2004-2019. The data analysis method used is the autoregressive distributed lag (ARDL) method. The results of the study found that the use of renewable energy in the short and long term has a positive effect on Indonesia’s economic growth. Meanwhile, the same thing is also shown from the FDI variable in the short term and long term which has a significant positive effect on economic growth and has a positive effect on economic growth. Finally, with this finding, it is concluded that both the short and long term the Indonesian government needs to make a breakthrough to explore renewable energy sources for economic growth.

scholarly journals The Effect of Higher Education on Growth: A Cointegration Analysis

2013 ◽  
Author(s):  
Murat Mustafa Kutlutürk ◽  
Hakan Kasım Akmaz ◽  
Ahmet Çetin
Keyword(s):  
Higher Education ◽  
Granger Causality ◽  
Cointegration Analysis ◽  
Annual Data ◽  
Causality Test ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Long Run ◽  
Short Run ◽  
The Relationship

In this study the relationship between higher education and economic growth was investigated using annual data between 1988 and 2012 for Turkey. To see short and long run effects of higher education on growth the Autoregressive Distributed Lag (ARDL) testing approach was used. In this investigation ratio of higher education graduates in employment was used as an explanatory variable. Zivot and Andrews test was implemented for the variables. The long and short run effects of higher education on growth was found significant. Granger causality test was implemented and one way Granger causality from higher education to growth was determined.

scholarly journals THE LINK BETWEEN CARBON EMISSIONS, AGRICULTURAL OUTPUT AND INDUSTRIAL OUTPUT: EVIDENCE FROM SOUTH AFRICA

2020 ◽  
Vol 21 (2) ◽  
pp. 301-316 ◽  
Author(s):  
Mabutho Sibanda ◽  
Hlengiwe Ndlela
Keyword(s):  
Climate Change ◽  
South Africa ◽  
Carbon Emissions ◽  
Industrial Sector ◽  
Industrial Output ◽  
Agricultural Output ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Annual Frequency ◽  
The Relationship

This study seeks to establish the relationship between carbon emissions, agricultural output and industrial output in South Africa. It uses data from 1960 to 2017 based on an annual frequency, giving a total of 58 annual observations. The Autoregressive Distributed Lag technique is employed to estimate the model on a bivariate basis. The evidence shows that carbon emissions are not influenced by agricultural and industrial output. Conversely, agricultural output is influenced by carbon emissions and industrial output. The results suggest that climate change resulting from carbon emissions has led to reduced agricultural output, adversely affecting food security. The significant relationship between industrial and agricultural output suggests that a properly functioning industrial sector will cause an increase in the agricultural output. The study’s findings have implications for climate change and manufacturing policies in South Africa.

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