Skip to main content

Future of International laws-Paris conference.


Paris Agreement, which was adopted during the 21st Conference of the Parties (COP 21) to the United Nations Framework Convention on Climate Change (UNFCCC) held in Paris in 2015. 

The Paris Agreement is a landmark international treaty that addresses climate change and aims to limit global warming well below 2 degrees Celsius above pre-industrial levels and pursue efforts to limit it to 1.5 degrees Celsius. It represents a collective commitment by nations to combat climate change, reduce greenhouse gas emissions, and adapt to its impacts.

Key aspects of the Paris Agreement include:

1. Nationally Determined Contributions (NDCs): Countries are required to submit their individual NDCs, which outline their efforts to reduce emissions and adapt to climate change. These contributions are intended to be ambitious and represent a country's efforts to achieve the overall objectives of the agreement.

2. Global Stocktake: The agreement establishes a process for a regular global stocktake to assess collective progress towards achieving the long-term goals of the agreement. This stocktake helps identify gaps and provides an opportunity for countries to enhance their climate actions.

3. Transparency Framework: The Paris Agreement emphasizes the importance of transparency and accountability. It establishes a robust transparency framework, requiring countries to regularly report on their emissions and implementation efforts, thus ensuring transparency and comparability of actions.

4. Adaptation and Loss & Damage: The agreement recognizes the need to strengthen adaptation efforts and support vulnerable countries in coping with the impacts of climate change. It also recognizes the concept of loss and damage associated with the adverse effects of climate change, including the impacts of extreme weather events and slow-onset events.

5. Climate Finance: The agreement calls for financial support from developed countries to assist developing countries in both mitigation and adaptation efforts. It aims to mobilize financial resources to address climate change, with a commitment to providing $100 billion annually by 2020, with a subsequent increase in funding in the future.

The Paris Agreement has garnered significant international support, with the majority of countries ratifying or acceding to it. It represents a collective effort to address climate change and transition toward a low-carbon, climate-resilient future. The agreement has helped shape global action on climate change and has influenced domestic policies and strategies worldwide. However, it is important to note that the effectiveness of the Paris Agreement will ultimately depend on the commitment and implementation of its provisions by the participating countries.






Comments

Popular posts from this blog

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification:   - You have prior knowledge about the c...

Supervised Classification

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...

Pre During and Post Disaster

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...

Hazard Mapping Spatial Planning Evacuation Planning GIS

Geographic Information Systems (GIS) play a pivotal role in disaster management by providing the tools and frameworks necessary for effective hazard mapping, spatial planning, and evacuation planning. These concepts are integral for understanding disaster risks, preparing for potential hazards, and ensuring that resources are efficiently allocated during and after a disaster. 1. Hazard Mapping: Concept: Hazard mapping involves the process of identifying, assessing, and visually representing the geographical areas that are at risk of certain natural or human-made hazards. Hazard maps display the probability, intensity, and potential impact of specific hazards (e.g., floods, earthquakes, hurricanes, landslides) within a given area. Terminologies: Hazard Zone: An area identified as being vulnerable to a particular hazard (e.g., flood zones, seismic zones). Hazard Risk: The likelihood of a disaster occurring in a specific location, influenced by factors like geography, climate, an...

History of GIS

The history of Geographic Information Systems (GIS) is rooted in early efforts to understand spatial relationships and patterns, long before the advent of digital computers. While modern GIS emerged in the mid-20th century with advances in computing, its conceptual foundations lie in cartography, spatial analysis, and thematic mapping. Early Roots of Spatial Analysis (Pre-1960s) One of the earliest documented applications of spatial analysis dates back to  1832 , when  Charles Picquet , a French geographer and cartographer, produced a cholera mortality map of Paris. In his report  Rapport sur la marche et les effets du cholĂ©ra dans Paris et le dĂ©partement de la Seine , Picquet used graduated color shading to represent cholera deaths per 1,000 inhabitants across 48 districts. This work is widely regarded as an early example of choropleth mapping and thematic cartography applied to epidemiology. A landmark moment in the history of spatial analysis occurred in  1854 , when  John Snow  inv...