This project is to explore if there is any connection between SDG12.1.1 on SCP mainstreaming and SDG 12.2.1, 12.2.2, 6.4.1, and 7.3.1. Because SDG12.1.1 only has data for 2017, only one year is analyzed.

Data Sources

The data was downloaded from the Global SDG Indicators Database and from the IRP Material Flows Database:

indicator description source units year
12.1.1 SCP policy instruments Global SDG Indicators Database 1=YES; 0=NO 2017
12.2.1 material footprint IRP Global Material Flows Database tonnes 2017
12.2.2 domestic material consumption IRP Global Material Flows Database tonnes 2017
6.4.1 water efficiency Global SDG Indicators Database United States dollars per cubic meter 2015
7.3.1 energy intensity Global SDG Indicators Database megajoules per constant 2011 purchasing power parity GDP 2016

Cleaning and standardizing the data

71 countries report on national SCP policy instruments. Countries that do not report (not in the 12.1.1 database were assumed to not have national SCP policy instruments.) We need to turn the SDG12.1.1 indicators into factor variables and get the log of the material footprint (mf) and domestic material consumption (dmc). We will also create a new variable scp.all that adds all the separate SCP mechanisms.

## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
## 'data.frame':    186 obs. of  13 variables:
##  $ country                      : Factor w/ 186 levels "Afghanistan",..: 180 7 120 29 131 10 76 51 78 82 ...
##  $ income.level                 : Factor w/ 4 levels "H","L","LM","UM": 3 4 2 3 3 4 3 3 4 4 ...
##  $ coordination.mechanism       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ macro.policy                 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ policy.instrument            : Factor w/ 2 levels "0","1": 1 1 1 1 2 2 1 1 1 2 ...
##  $ national.action.plan         : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 2 1 1 1 ...
##  $ material.footprint           : num  1.92e+08 2.39e+07 6.86e+07 5.79e+07 4.61e+08 ...
##  $ domestic.material.consumption: num  2.90e+08 3.25e+07 7.34e+07 8.47e+07 4.17e+08 ...
##  $ water.use.efficiency         : num  1.2 1.9 2.4 3.3 3.5 3.7 3.9 4.6 5.2 6.2 ...
##  $ energy.intensity             : num  8.18 5.29 6.45 5.81 3.07 3.81 3.43 3.65 3.85 5.19 ...
##  $ scp.all                      : Factor w/ 2 levels "0","1": 1 1 1 1 2 2 2 1 1 2 ...
##  $ log.mf                       : num  19.1 17 18 17.9 19.9 ...
##  $ log.dmc                      : num  19.5 17.3 18.1 18.3 19.8 ...

Exploratory Data Analysis

Let’s make some boxplots.

Material Footprint

Countries with SCP policies seem to have higher material footprint than countries without. Especially countries with sustainable consumption and production (SCP) national action plans or SCP mainstreamed as a priority or target into national policies.

Domestic Material Consumption

Countries with SCP policies seem to have higher domestic material consumption than countries without. Especially countries with sustainable consumption and production (SCP) national action plans or SCP mainstreamed as a priority or target into national policies and countries with policy instrument for sustainable consumption and production.

Water Use Efficiency

Countries with SCP policies seem to have higher water use efficiency than countries without. In other words, in coutries that have SCP policies, there is a higher value added of a given major sector relative to the volume of water used.

Energy Intensity

There seems to be no difference in energy intensity between countries with SCP policies and countries without.

Aggregated 12.1.1

We can also look at the aggregated 12.1.1 data. scp.all is a factor variable of value 0 (no SCP policy instrument reported) or 1 (at least one SCP policy instrument reported)

Disaggregate by income level

Let’s now look at each indicator in more depth over time.

Time Series Analysis

Material Footprint (SDG 12.2.1)

Tidy the data such that each column is a different variable.

Plot each country material footprint across time

Plot material footprint across time by scp (YES=1, NO=0) and income level (H=high income, L=low income, LM=lower medium income, UM=upper medium income) after the year 2000.

For each income group, countries reporting on scp policies have higher material footprint then countries not reporting on scp policies. THe biggest difference in between countries in the upper middle (“UM”) income group.

We can also look at the changes in rates of material footprint.

rates1990.2017 rates1995.2005 rates2000.2010 rates2005.2017
H0 0.0190552 0.0265462 0.0207946 0.0029283
H1 0.0219397 0.0285687 0.0275752 0.0078696
L0 0.0345533 0.0239565 0.0550348 0.0615898
L1 0.0226221 0.0111490 0.0524831 0.0529974
LM0 0.0448592 0.0291336 0.0840701 0.0589924
LM1 0.0244094 0.0147652 0.0428400 0.0381472
UM0 0.0251251 0.0198603 0.0425541 0.0353506
UM1 0.0323875 0.0232757 0.0499056 0.0314324

Domestic Material Consumption (SDG 12.2.2)

rates1990.2017 rates1995.2005 rates2000.2010 rates2005.2017
H0 0.0176115 0.0318821 0.0151941 -0.0070162
H1 0.0115897 0.0271772 0.0155740 -0.0018644
L0 0.0286553 0.0362225 0.0388322 0.0292113
L1 0.0358157 0.0269430 0.0392797 0.0355769
LM0 0.0428180 0.0374444 0.0546209 0.0441977
LM1 0.0305818 0.0297262 0.0306484 0.0322268
UM0 0.0272660 0.0245514 0.0318637 0.0225103
UM1 0.0214034 0.0267829 0.0166300 0.0139972

Water Use Efficiency (SDG 6.4.1)

Not enough data

Energy Intensity (SDG 7.3.1)

rates2000.2015 rates2000.2010 rates2005.2015
H0 -0.0415086 -0.0286113 -0.0344545
H1 -0.0881209 -0.0924356 -0.0756515
L0 -0.1479168 -0.1039481 -0.2093567
L1 -0.2166585 -0.1218081 -0.2885556
LM0 -0.1897803 -0.2577524 -0.1405423
LM1 -0.1245098 -0.1372562 -0.1178678
UM0 -0.1224201 -0.1599209 -0.0912134
UM1 -0.0991877 -0.1250361 -0.0913377