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O'Neill ( 2014 ) illustrate various futures for SSPs 1–5 in terms of challenges in mitigation and adaptation to climate change. Each SSP contains qualitative and quantitative information. SSP1, which describes 'sustainability', assumes low population growth and high economic growth. It also assumes high levels of education, governance, globalization, international cooperation, technological development, and environmental awareness. By contrast, SSP3, which describes 'fragmentation', assumes high population growth and low economic growth. It also assumes low levels of education and governance, regionalization, and low environmental awareness. SSP2 describes a 'middle-of-the-road' scenario between SSP1 and SSP3. In this study, SSP4, which assumes inequality, was interpreted such that optimistic scenarios like SSP1 are true for high-income countries, whereas pessimistic scenarios like SSP3 are true for low-income countries. Technology advances mainly in high-income countries, resulting in few mitigation challenges. In contrast, in low-income countries, poverty does not improve, and large numbers of people do not benefit from economic growth and remain vulnerable to climate change. SSP5 assumes low population growth, high economic growth, and high human development; however, environmental awareness is low, and there is a high degree of dependence on fossil fuels. Adaptations are easy to implement because of improved human capital in developing countries. SSPs 1 and 5 depict relatively optimistic scenarios with respect to hunger risk, whereas SSPs 3 and 4 depict relatively pessimistic scenarios. SSP2 falls between them.

The AIM/CGE model builds on the work by Fujimori (in press) and Fujimori ( 2012 ), and has recently been used in several studies (Hasegawa 2014 , Nelson 2014 , von Lampe 2014 ). Supply, demand, trade, and investment are described in individual behavioral functions that respond to changes in the price of production factors and commodities as well as changes in technology. The functions also respond to preference parameters on the basis of assumed population, GDP, and consumer preferences. The supplementary material in chapter S3 provides details on the CGE model and parameter settings.

This paper focuses on the exogenous and endogenous responses of the model. Conceptually, a given population and income growth shift rightward the demand curve, increasing food demand and raising prices. Producers respond to the price increase by increasing production by expanding cultivated areas and pastures, and by increasing land productivity (production per unit land area) under a given land productivity and limited land. Consumers respond to the price increase by decreasing consumption, and shifting to less expensive goods. Some might face risk of hunger if they consume insufficient food. International trade globally reallocates production and consumption, decreasing the price of food and contributing to lower hunger risk. The CGE model covers the full economy and captures these general response options. Further details about model attribution are provided in Robinson ( 2014 ) and Valin ( 2014 ).

The population at risk of hunger is calculated outside the CGE model using the FAO approach (FAO Women Shoes/Sneakers Scout Etnies zZjNPE7
). Food consumption varies among households within a country and people who eat less than the minimum energy requirement face a risk of hunger. The CGE model calculated mean per-capita food consumption for a representative household. Then, the proportion of the population at risk of hunger was estimated from the mean per-capita food consumption, the minimum energy requirement, and the coefficient of variation (CV) of distribution of dietary energy consumption among households in a country (FAO 2008 ). Future changes in food distribution inequality were described by changing the CV and income growth. Future demographic changes were considered by calculating the mean minimum energy requirement of a country from its demographic structure (IIASA 2012 ) and minimum energy requirement by gender and age (FAO/WHO 1973 ). More details are provided in the supplementary material of chapter S1.

Scenarios developed in this study were determined from the perspective of adaptation challenges because hunger risk is strongly related to these rather than mitigation challenges. Only elements related to hunger risk (i.e., meat diets) were used as parameters of the model (i.e., income elasticity of meat demand) (table 1 ). For population and income, we used SSP assumptions (IIASA 2012 ).

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opsort:List{X}->List{X}. opsort:NeList{X}->NeList{X}. eqsort(nil)=nil. eqsort(E)=E. eqsort(EN)=$sort($split(EN,nil,nil)). op$sort:$Split{X}->List{X}. eq$sort($split(nil,L,L’))=$merge(sort(L),sort(L’),nil).

The auxiliary operation $split has three arguments: the first one is the list to be split and the other two are accumulators (initially both empty) that keep the elements as they are moved from the main list into the appropriate sublists. In this way, we have an efficient tail-recursive definition.

op$split:List{X}List{X}List{X}->$Split{X}[ctor]. eq$split(E,A,A’)=$split(nil,AE,A’). eq$split(ELE’,A,A’)=$split(L,AE,E’A’).

The auxiliary operation $merge also has three arguments, but now the first two are the lists to be merged and the third one is the accumulator where the result is incrementally computed by means of another efficient tail-recursive definition.

The module also provides an operation merge that simply calls the previous operation with the empty accumulator. Notice that if both lists are sorted then the result of calling merge on them is a sorted list, but in general merge is a total function that can be called on any two lists whatsoever.

opmerge:List{X}List{X}->List{X}. opmerge:NeList{X}List{X}->NeList{X}. opmerge:List{X}NeList{X}->NeList{X}. eqmerge(L,L’)=$merge(L,L’,nil). op$merge:List{X}List{X}List{X}->List{X}. eq$merge(L,nil,A)=AL. eq$merge(nil,L,A)=AL. eq$merge(EL,E’L’,A) =ifE’<E then$merge(EL,L’,AE’) else$merge(L,E’L’,AE) fi. endfm

The Maude prelude also provides another predefined module for sorting lists, namely, SORTABLE-LIST , where the required order is strict and total, as specified in the predefined theory STRICT-TOTAL-ORDER of Section 28 Parker Cut Off Shorts in Blue size 24 also in 25262728293031 A Gold E Parker Cut Off Shorts in Blue. - size 24 (also in 25 30 29 FDqsj0
. Since the theory STRICT-TOTAL-ORDER is a strengthening of STRICT-WEAK-ORDER with the additional requirement of totality, we can use it as a parameter theory to specialize our WEAKLY-SORTABLE-LIST module to strict total orders, thus getting the SORTABLE-LIST module. For this we need a view from the theory STRICT-WEAK-ORDER into the theory STRICT-TOTAL-ORDER , which is precisely the predefined inclusion view STRICT-TOTAL-ORDER in Section 7.11.3 .

Moreover, since we also use another renaming to have more convenient sort names, the construction of the parameterized module SORTABLE-LIST on top of WEAKLY-SORTABLE-LIST mirrors the process of constructing WEAKLY-SORTABLE-LIST on top of LIST , as described in Figure 7.4 , where the sort renaming has been abbreviated to α , WEAKLY-SORTABLE-LIST to W-S-LIST , STRICT-WEAK-ORDER to S-W-O , and STRICT-TOTAL-ORDER to S-T-O . The reader should compare this figure with Figure Womens Damen Sandalette Plateau Rieker Z94KJB3s
to appreciate the similarity between both.

You can export your products to a CSV file if you want to accomplish the following goals:

Any changes that you make to the products in your product CSV file appear in your store's product list if you import your product CSV file into the Shopify admin .

The easiest way to edit multiple products or product variants simultaneously is to use the bulk editor in your Shopify admin.

Overview

Learn more about how to export products from your Shopify admin:

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If you export up to one page of products (up to 50 products), then the CSV file is downloaded by your browser. If you export more than one page of products (51 or more products), then the CSV file is emailed to you and the Shopify account owner.

To export your products:

From your Shopify admin, go to Products .

Products

If you want to export only some of your products, then you can Womens Selena Pm1 Wallets Le Temps Des Cerises 89mm3TLM
to view and select specific products for export.

Click Export .

Export

From the dialog box, choose the products you want to export:

Select which type of CSV file you want to export:

Click Export products .

Export products

Sorting your CSV file in a spreadsheet program can cause product variants or image URLs to become disassociated. Importing a CSV file that has been sorted might overwrite your existing products with bad data, which cannot be recovered.

If you move your products to a new website and plan to close your current store or delete some of its product listings, then you need to upload your product CSV file to your new store while your old store and its product listings remain viewable.

When you export your products, the images associated with each product are not included in the CSV file. If you import the products to a new store, then the product images are generated only if they remain viewable on a publicly accessible website.

To ensure that your product images remain accessible to you, leave your original store and its products intact until you finish the import process. This allows your new store to assign the correct product image to each listing.

If you don't have a spreadsheet program, then you can edit CSV files using a text editor.

You should only use a text editor as a last resort. For example, use a text editor if there are formatting errors in your CSV file and you cannot open it in a spreadsheet program.

Most computers have text editors that can open CSV files. You can also use a program like cropped tailored trousers Blue Joseph Z2wKPWIoY
or Charm Bracelet Pastel Abstract 4 by VIDA VIDA VwocIH
to edit and debug your CSV file.

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