scholarly journals Climate Change Impact on Togo’s Agriculture Performance: A Ricardian Analysis Based on Time Series Data

2013 ◽  
Vol 6 (4) ◽  
Author(s):  
P Mikémina
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Change Impact ◽  
Time Series Data ◽  
Series Data ◽  
Change Impact

scholarly journals Splendide Mendax: False Label Claims About High and Rising Alcohol Content of Wine

2015 ◽  
Vol 10 (3) ◽  
pp. 275-313 ◽  
Author(s):  
Julian M. Alston ◽  
Kate B. Fuller ◽  
James T. Lapsley ◽  
George Soleas ◽  
Kabir P. Tumber
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Time Series Data ◽  
Consumer Preferences ◽  
Series Data ◽  
Alcohol Content ◽  
Demand Shifts ◽  
Expert Ratings ◽  
Jel Classifications

AbstractAre wine alcohol labels accurate? If not, why? We explore the high and rising alcohol content of wine and examine incentives for false labeling, including the roles of climate, evolving consumer preferences, and expert ratings. We draw on international time-series data from a large number of countries that experienced different patterns of climate change and influences of policy and demand shifts. We find systematic patterns that suggest that rising wine alcohol content may be a nuisance by-product of producer responses to perceived market preferences for wines having more-intense flavours, possibly in conjunction with evolving climate. (JEL Classifications: D22, L15, L66, Q18, Q54).

scholarly journals Climate Change Driven Food Inflation in Ethiopia?

2018 ◽  
Vol 8 (1) ◽  
pp. 13-22
Author(s):  
Berhe Gebregewergs Hagos
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Average Price ◽  
Ceteris Paribus ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Lag Model ◽  
Food Inflation

The research dealt with the relationships between temperature variability and price of food stuffs in Tigrai using 84 months collected time series data thereby applied a Univariate econometric tool and finite Distributed Lag Model in defining the variables and outcome of the study. As a result, the econometric regression analysis witnessed that a 1oC temperature rise contributed the average price of food stuffs such as barley price rose up by 80 percent, maize 186 percent, sorghum close to 275 percent, wheat 60 percent, and 170 percent in white Teff over the years, ceteris paribus.

scholarly journals Net zero 2050 as an EU priroty: modeling a system for efficient investments in eco innovation for climate change mitigation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jelena Jesic ◽  
Andrea Okanovic ◽  
Andrea Andrejevic Panic
Keyword(s):  
Climate Change ◽  
Developing Countries ◽  
Time Series Data ◽  
Sample Selection ◽  
Series Data ◽  
Research And Innovation ◽  
Net Zero ◽  
Study Results ◽  
Change Impact ◽  
New Evidence

Abstract Background The study background is based on the fact that The Recovery Plan for Europe envisages investing 30% of the huge budget in climate change, with the goal of zero gas emission by 2050. This ambitious plan will require (for now indefinitely) investments in research and innovation. The study’s main objective is to check and analyze the existing and propose a new model of effective investments in eco-innovation. This will contribute to effective long-term investment policy, climate change impact, and mitigation of consequences. Methods The basic methodological tools for solving the problems discussed in this study were correlation analysis, regression analysis, and paired sample t-test. All calculations were performed in the SPSS 20 statistical software. Time series data of the selected indicators were obtained from the European Innovation Scoreboard 2020. The database used to collect the data for the EU member countries and selected third countries for the analysis is the European Innovation Scoreboard 2020. To avoid sample selection bias, the authors considered all of the available data for all the member countries and selected third countries in the European Innovation Scoreboard 2020 for the 2012 to 2019 period. Results The study results show the path developing countries should follow in directing their inevitable and increasing eco-innovation investments, taking into account the arguments of structural differences in financing Research and Development (R&D). The authors’ findings support the thesis that investments in R&D is low in developing countries, while in developed EU countries, there are more investments in R&D from the business sector for the 2012–2019 period. Conclusions Study conclusions are summarized as a proposal of the appropriate R&D financing model approach to developing countries with a greater share of eco-innovation and self-sustainable R&D financing for climate preserving products. This study is important as it provides new evidence on financing R&D investments in innovation leader countries and emerging innovator countries according to Summary Innovation Index.

Net Zero 2050 As An Eu Priroty – Modeling a System for Efficient Investment in Eco Innovation for Climate Change Mitigation

2021 ◽  
Author(s):  
Jelena Jesic ◽  
Andrea Okanovic ◽  
Andrea Andrejevic Panic
Keyword(s):  
Climate Change ◽  
Developing Countries ◽  
Time Series Data ◽  
Sample Selection ◽  
Series Data ◽  
Research And Innovation ◽  
Catching Up ◽  
Net Zero ◽  
Change Impact ◽  
New Evidence

Abstract Background: Study background is based on the fact that the EU recovery plan envisages investing 30% of the huge budget in climate change, with the goal of zero gas emission by 2050. This ambitious plan will require (for now indefinitely) investment in research and innovation. The main objective of the study is to check and analyze the existing and propose a new model of effective investment in eco-innovation, on the basis of which a contribution to effective long-term investment policy, climate change impact and mitigation of consequences will be given. Methods:. The basic methodological tools for solution of the problems discussed in this study were correlation analysis, regression analysis and pared sample t-test. All calculations were performed in the statistical software SPSS 20. Time series data of the selected indicators were obtained from the European Innovation Scoreboard 2020. Database used to collect the data for EU member countries and selected third countries for the conducted analysis is the European Innovation Scoreboard 2020. To avoid sample selection bias, we considered all of the available data for all member countries and selected third countries in European Innovation Scoreboard 2020 for period 2012 to 2019.Results: Results of the study are showing the path which developing countries should direct their inevitable and increasing eco-innovation investments, taking into account the arguments of structural differences in financing R&D. According to European Eco-innovation Scoreboard the best eco-innovation performers are Luxembourg, Denmark, Finland, Sweden and Austria. While countries catching up with eco-innovations are Lithuania, Greece, Estonia, Malta, Croatia, Slovakia, Poland, Romania, Cyprus, Hungary, Bulgaria. Conclusions: Study conclusions are summarized as proposal of appropriate approach of R&D financing model to developing countries with a greater share of eco-innovation and self-sustainable R&D financing for climate preserving products. This study is important as it provides the new evidence on financing R&D investments in leading and developing countries according to Innovation Scoreboards.

A daily burned area mapping method using AVHRR time-series data

2021 ◽  
Author(s):  
Shuhan Lou ◽  
Yuanhong Liao ◽  
Yufu Liu ◽  
Yuqi Bai
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Time Series Data ◽  
Mapping Method ◽  
Series Data ◽  
Burned Area ◽  
Burnt Area ◽  
Area Data

<p>The study of complex interactions between fire and atmospheric dynamics of the earth system is drawing increasing attention in recent years, especially when fire seasons are extended due to global warming, where the historical daily burnt area data played a pivotal role in analyzing wildfire regimes change. Existing products could not fully meet the temporal requirements: daily burnt area data in global fire emissions database (GFED4) starts from mid-2000 using MODIS while ESA Fire Climate Change Initiative (FireCCILT10) Dataset from 1982 to 2017 is provided on a monthly grid.</p><p>Advanced Very High Resolution Radiometer (AVHRR) series of sensors are widely used to develop pre‐MODIS daily historical records. However, compared to MODIS, the AVHRR sensor has a lower radiometric and geometric quality and is missing Short Wave Infrared (SWIR) band. To address the data quality problem, this research study presents a time-series mapping method for daily burned area using AVHRR composite. Daily fire-sensitive indices are calculated to develop a time-series data composite which is masked by the burnable surface of GLASS_GLC land cover product. Then, Continuous Change Detection and Classification (CCDC) time-series model, which originally implemented on Landsat data monitoring land cover change, is revised to detect an abrupt change in the time-series data composite and remove noise, ensuring temporal consistency. The image of a time-series breakpoint is further classified using a spatial contextual method to distinguish biomass burning from other forest degradation change like a landslide and is used to generate burned area probability map.</p><p>The methodology is verified in California, US, where fuel aridity increased during 1984–2015 driven by anthropogenic climate change. The samples are collected based on the National Monitoring Trends in Burn Severity（MTBS）Burned Areas Boundaries Dataset from 1984 – 2018 and California Department of Forestry and Fire Protection's Fire and Resource Assessment Program (FRAP) fire perimeters from since 1950. Primary results show that the proposed method can effectively detect burned area on daily basis with CCDC algorithm reducing the complexity of change detection.</p>

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